{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abb7b349",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566880c3",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 男1000米跑、男引体进行等宽分箱操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0a4a6fa6",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "maleScore =pd.read_excel('./体测分数_男生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "efa9a569",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maleScore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ccc89d54",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "优秀     380\n",
       "及格      68\n",
       "不及格     29\n",
       "Name: 男1000米跑分数, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_run1000 = pd.cut(maleScore['男1000米跑分数'],bins=3,labels=['不及格','及格','优秀']).value_counts()\n",
    "male_run1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56eb6625",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "优秀     219\n",
       "及格     137\n",
       "不及格    121\n",
       "Name: 男引体分数, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_pullUp = pd.cut(maleScore['男引体分数'],bins=3,labels=['不及格','及格','优秀']).value_counts()\n",
    "male_pullUp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08b97718",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams['font.family']='Microsoft Yahei'\n",
    "plt.rcParams['font.size']=14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d3aba38",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1705ebe4400>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qu80/46wxjFyVhU3bmmucmdZQnoe3vgqvq67N/WyR/VHKR0M7Y3SjRswgdsJxbF9yByPO/SfZnz/OsLPuI3bC8ZSu/3yfr2PQidcx6MTr2PHu3QCkzL6KlNlXtdjHEh6Lp1n/N6APEfO34kL7DwegJndj07ZGyuvB52p5ktLbUEvp+s+xhMfQ/5AzQTOhfN6mkQo+Vx3K66YmewOD5tyAJTyWqBEzqN71S1PYaxYbQ/9wB9W71mKyhND/kLPw1FdR/PMH+pOYTNQWbqO+aAded13TJ5r871/RR490UTjHTZpL1PBD2fLaTZisIYQljsZdXYy3oRZzSMReH9tQsgvNZMEWk4SrshBXZWGbn+6skQkttvs8DVQ6V2OPG4yntgJvXUWrx4jOk4Buxh6duNdJKqPnP9nufQUr3iDny/bv7w61+b8TNWIG9thBLWaHRSSPR/m81Pj7TWvzf2+aGdh8GFdEynjcteU0lGQ17ReWNLbV84SnjKeuYCvK0zpQwlMm4vjDHWR/+RQN/pOJ7qpidn30EI5T/47XVUdFGycem3N+kEHpuk8ASJh2uv79+s8w28PxueuxRsQRN2kutfmb2z1GSMJQlM9LfUnrE5Imi73VWPLo0TNb9MUCLUYv5C5fSP63L1JXtJ26wu0MmH4OMWOPJuer3S3UyGGHEpY4CtBPIjeUZlO05j39Ts2MNTy21ZhtoCngu8qAwy9A+bwMPT0dS0gEXlcdO96+k5hxxxI3ac5eH7vm/qOxxw1i3GUL97qf4+S0du9r71OV6DwJ6GYaynLanGAyeO6N2KIT2frazW0+bsyl/8VTW97N1bVWvmk5A2fOp/+hZ5H1yaOA3h0TO/EEKrb+0HQCsXzTMuImnkjC1D9Q8KM+ptoWlUjUiMMpXvsBjaNAyjctY9AJfyFm7DGUbfwagLCkcYQnjSX78wWtnj9qxAwcp/6dkl8+onjNey1aWOWblpHTL56hp99JzpdPUrSq/SnKbdFMZiZd9w6aWf8vWl+aTcH3rxJ/0CmUbvii1Xjh2HGzqc5e3+YfEZMttNWJxLLfvqJs41JGz3+Sqp1ryP1an8hijYhj3BUvtfg4X/zz+wyecyOe+mpKN+z+RFC96xe2vn4z9cU7W0z1BwgdMBzNZKahpO1uBktY9F7/z9iiB3Z4VmHhT2+imczUF++kvnhnUzdczLhjKV3/Oc7372v1mH6OaYw872FA7/vfc3SStV8CQ09Px1WRT/ToI6nY8h0hcUPY9tbtuMpzO1SX6DwJ6D3s+bEzbOAYYsbPZlfmP9v9SGqNiMdd3fkui/1VV7iNkl8/IWHqaZjtEdQVbSd2/HGYrKHkfL175lzFlu+p2rmWpKP/D1vMQNyVRcRNScVTX0n+dy837Ve8NpOEqacx5ORbCEsai8/TQMJBp1JXuI2i5mN3TWYGzvwTiUdcSOHKN8n58uk26yv6aQk+Vx2D5txAvyHTyP5iQYuhi400s5nQhGF4m42VVl43G56+AJMtFOVx0VCehzU8lsFz/0pN9gbqinb3A4ckDCVy2CHs/Ljtk5ONLfE9RaRMILT/MBrKsgmJH0JDWS5DTrmNKufqFmOSG8dA1xftaPGHwVtfpU+OMZkJSxxNeMo4LKFR+oiQiSdSX5rdZgsaYOQFj1G8+r1WJ/3MoVEkH3sFYQNG6if5OqB49bstvrfHJLda52R/xE1OJfmYyyle+yG5S59jys2fUvzzh1j7JTB6/gIKV7xB4U9LuvyTgGhNAnovoscezZC5N1Hx+3ft9qWaQ6OwhETQUL73xYGAZv3XXXcdyF0fP4yntpzYiScQNfJwanM34nz//lb9sNve+jsps/9MzJij0UxmqnauJfuLBS1accrTwJZXbyTlhL8QPyUV5fNRsfUHcr58oqllGp4ygcFz/4otKpFdHz+yz7HcJb98RH1JFkNSb2bcFS+x4510LOGxaCYzjtNuJyxxFJFDp+Hzusn65DG8DTWE9h8KmqlFmGsWO/2nn42ntqJFOFvCohh6+p3UFW5rmubcnGa2Yo9NoaGsdauvOutXNjx1PolHXMCYS57FXVUMJhMbn72kaR973GCG/uEO6kt2EZ4ynuTjribniycAiJ96KrHjjycscRSaxUp9kZPSDV8QnjKBhKmnke3fT//htnzPLaFR+JqN1jHbQrElDGP8FS/hqipi18cP7/Xn2shkCyNy6DTCBo7x30ajmczkLX+hQ49vopmJGXMUiUdciGa2suPdu1qtP1K67hNq8zcz5OQ0+h92LqW/fkr579/qXWltTLYSnScBvQfNYid61BEkTDud8ORxFP60pGW/44gZeOur9REHJhMDps/DXVtObW7b/aP9Dz0bn8eFz9NA3KS5+Dyu1ie79qH5Qkx7Ul43OV89Tc5XbbdiG/kaatj10UPs+uihve7nri5pc1ZgI5PFjruqmG1v/r3DH3Vrstex8blLiJt8EpXbf2L0RU9RV7gNn6ue3KX/pTbvd+qKtqO8bjx1FQw97R8MmN56EoTP4yL7890jYMKTxzHklL9jstj4/X9p+jTnmGRs0Ul46ytBQfy009A0jeqdP7dZm7e+GldFAT5XHT6vi5CoQQw/+352ffwwZnsYw868B1dVEVtevp64ySeRctyfsYT0a5q6Xle0ncKVb1K1ay3eukpiJ57IiHkPUrl9JUXNWraeukrCk8YQEj+EkIShWMNjmoapmUMj/UujauR9s0ifut/BBY/MIREMmvtXarLWU7l9JbnLntf76n1ehpycRuyE44mdcPxejxE6YAQj5j0IaBT8+DpFq95G+Txt7ltftIPNC68gesws+h9yJv2nn03u0ueaZmiKriUB3UzYwNGMukBfb6Js0zJ2ffJIq7G5iYefT3jyODTNpI9sKM1mxzvp7XZ/xE85uWk9YHdtud6XG8StjSrn6lZLlHaE8rop9p9A2/T8/7W7X8Xv3/LrY6fpQ+yaj+FVPn2Si7+LIenoyxgw449UZ/2K8737mvpdbdEDGX72fZgs+szDhrJcnO/fh7u6pMXzhKdMZMD0efQbOg1PTRm7Pn2U8o1LCU0Y5l/udCopx11NXeE2tr15G96GagpXvoFmtpA061IsoVFse/PWFsccOOtSEmecR9Hqd/zdPrtbzcU/v0/U8EMZd/mLKJ+Xii3fN03J99ZVkv/9K5RvWrbfY4vdlYWse+wP7d5f/vt3LYbeNb3+5HFNJ/7qCrbifP9+qrPWdXhkSfmmZZRvWqaPrumCEUmibZpSXfdxO1Csuf/oA35R4SkTqSvYuu/ZaZoJ0Dq+tKN/6rPoGtbI/oQljtr7CBHN3O77EzpgBAkHn0HZhi+ocv7Mnt1OYUnjiJt4PNlfPNmqr7Xf0IPx1ldRm9fyU5PJFoo9amCLLhixb1NvW6oZXUOgkoAWQhhKArp9cskrIYQIUBLQQggRoCSghRAiQElACyFEgOqtAV2w712EEAFAflf3oleO4hBCiN6gt7aghRAi6ElACyFEgJKAFkKIACUBLYQQAUoCWgghApQEtBBCBCgJaCGECFAS0EIIEaAkoIUQIkBJQAshRICSgBZCiAAlAS2EEAFKAloIIQKUBLQQQgQoCWghhAhQEtBCCBGgJKCFECJASUALIUSAkoAWQogAJQEthBABSgJaCCEClAS0EEIEKAloIYQIUBLQQggRoCSghRAiQElACyFEgJKAFkKIACUBLYQQAcpidAFCONIyTUA0EAfE+r+GAmZAQ29IaIAC3IDH/9UN1AJFQKEzI7Wsp2sXojtpSimjaxC9mCMtsz8w2n8bBSSxO4gbwziKrvk05waKgcI2bnnAJuA3Z0ZqVRc8lxDdTgJadJojLdPO7hBuDOLGr9HGVdaubOA3/21D47+dGanlRhYlxJ4koMV+c6RlxgMzgSP8X6cCNkOL6hp5wDrgG2AZsNKZkdpgbEmiL5OAFvvkSMscze4wPgK9ZdwX1AM/oof1MuBHZ0ZqnbElib5EAlq04kjLjAVSgZOBY4AEYysKGC5gJXpYfwl848xI9RhbkujNJKAFAI60zBHA6cApwOHoIyjE3pUCHwLvAJ9K61p0NQnoPsyRljkKONt/m2xwOcGuFvgY+B/woYS16AoS0H2Mv/viImA+MMnYanqtKuB94DX0lrV0g4gDIgHdRzjSMo8ArgTOAkIMLqcvyQX+C/zXmZGaZ3QxIrhIQPdijrTMKOBC4ApggsHl9HVu4G1ggTMj9VujixHBQQK6F3KkZR6KHsrnAmEGlyNa+wV4AnjFmZFaa3QxInBJQPcijrTMVOAO4FCjaxEdUg4sBJ5wZqRuM7gWEYAkoHsBR1rmyejBfIjRtYgD4gVeAu5yZqTuNLoYETgkoIOYIy3zFPRgPtjoWkSXcAHPA/c6M1JzjS5GGE8COgj5g/lOYJrRtYhuUQ88DTzgzEgtNLoYYRwJ6CDiSMucC9yDBHNfUQM8DvxT1rrumySgg4AjLXMY8G/0tTFE31MB/At4SFbX61skoAOYIy0zBEgDbkEmlwj4HfizMyP1S6MLET1DAjpA+Udm/BsYZnQtIuC8CtzozEgtMLoQ0b0koAOMIy1zKPAfpDtD7F05cCvwjDMjVX6JeykJ6ADh7864Bb1LQ7ozREf9CFzpzEj9xehCRNeTgA4AjrTMqcDLwFijaxFByYP+qesOZ0ZqjdHFiK4jAW0gR1qmGf1j6h2A1eByRPDbCpzvzEhdaXQhomtIQBvEfwWTxcBhRtciehUPkI4+ycVncC2ikySgDeBIy7wAeAqIMLoW0Wt9A1zgzEjdZXQh4sBJQPcgR1pmBPoyk38yuhbRJ5QB850ZqR8YXYg4MBLQPcSRljkF/Xp1owwuRfQtCngYuFUuvRV8JKB7gCMt8xxgERBqcCmi7/oBmOfMSM0yuhDRcSajC+jtHGmZdwKvI+EsjDUD+Ml/tR0RJKQF3U38E08Wol92SohAUYd+8vBtowsR+yYt6G7gSMtMBJYh4SwCTyjwpiMt8yajCxH7Ji3oLuY/Gfg+MMjgUoTYl6eBa5wZqV6jCxFtk4DuQo60zD+gT9kON7gUITrqE+AcZ0ZqldGFiNaki6OLONIyrwfeRsJZBJc5wLeOtMwUowsRrUlAdwFHWmYa8CigGV2LEAdgErDCkZY5yehCREsS0J3kSMv8B/CA0XUI0UlJwFcS0oFFAroTHGmZdwN3G12HEF0kDvjSkZY50ehChM6wgNY07UtN005t9n2DpmlDNE0bq2nalft5LLumaXGapg3TNC2m66ttzZGW+QDwj554LiF6UDx6SI83uhBh0CgOTdMGAzsAh1Iqy7/NDQwHbOhnltcDFyilqjVNWwgcClj8N5v/ZgLcgAv9EvW1wH+UUou7s35HWua/gL9253MIYbBC4FhnRuoGowvpy4wK6HuBGUqp2c22+YAUpVSupmkJwLvAX5VSP/pbxRb0IHYBDcA/gWqlVHpP1u5Iy3wMuK4nn1MIgxQCxzgzUn8zupC+qscDWtO0MMAJXKmUetu/zYoevPFKqRL/Nk0ppTRNOxN9ic499UNfqau6jfuuU0r9r6trd6Rl/ge4tquPK0QAK0AP6Y1GF9IXGdEHfT2QAHysaZrSNE2hhzNAcbNtPv/XQ5RSiY034Ez07owN6Jf4KQRmNd+nm8L5FiScRd8zAH10x2ijC+mLerQFrWlaCnqwRgKhSql6//YxwGrgfOB2pdTBbTw2Av2k3GnAhcA89NbzCvSrkywEHlVKVXZ13Y60zHOBV5FxzqLv2gFMd2akFhldSF/S0y3oB4HP2tg+BGhznVpN0xI0TbsVaPyIdbBS6qfG+5VSHwMHA/2B7Zqm/VfTtCO7qmBHWuaR6Gs5SziLvmwo8J5/lUbRQ3o6oL8BLmtj+0Hof6Hbcj760J8n0cdpPqZp2nNAIjDC/+8M9Jl8k9D7t+1dUaz/Y927XXU8IYLcDOAFo4voS4waxaFo2cWxCngFPaTb6+JIAKLQZ+39hn5FbIBU4GrgUKVUeVfV6EjL7A/8iN5yEELsdrczI/VOo4voCwyfSahp2nRgGvoSnXved56maXcBKKWKlFJbAQfwjVJqq//7c4FkYKumacWapk3pbE2OtMww4AMknIVoyx2OtMzzjS6iLzA0oDVN04DHgfeVUtva2GUQMLLZ/hOB0cCqxm1KqRlKqXClVDxQjz5Z5YA50jJN6CcE5dJAQrTveUda5hFGF9HbGd2CfgiYCqT5v28ABmia1k/TtHDgOGAzgKZpNuDfwPN76cqIRb/UfGdkoI8UEUK0zw6860jLHGZ0Ib2ZkQF9Nvp06TuVUo0jNFaiXzOtEn0I3UjgJX//8zvofdBNfV+aptk0TYv3B/pp6C3o4gMtyL/g/t8O9PFC9DHxwIeOtMxIowvprYwM6KXAk0qp+xo3KKVKlFKjgDAgTCnl8P97K+ADTtxjnHMS+kSVSuA54AZ1gGc9/S2BRQfyWCH6sLHAM0YX0VsFxSWvNE2brpRasZf7tQMNZgBHWqYd+B69u0UIsf8ud2akPmt0Eb2N0X3QHbK3cPbf39m/Mo8i4SxEZ/zbkZY5wegiepugaEF3J0da5uno1xIUQnTORuBgZ0Zqp0ZSid2CogXdXfwXynzO6DqE6CXGAg8bXURv0mdb0P7xzl8CRxtcihC9zcnOjNRMo4voDfpyC/pvSDgL0R1e8C+VIDqpTwa0Iy1zOJBudB1C9FL9ka7DLtEnAxp4GpBlE4XoPqf411EXndDn+qAdaZkXsHslPCFE98kDxjgzUrv8Ihp9RZ9qQTvSMmOBR4yuQ4g+YiBwt9FFBLM+FdDoizMlGF2EEH3INY60zMlGFxGs+kwXhyMtcxb6+h9CiJ71A3CEMyO1b4RNF+oTLWj/WhuyoIsQxpgBXGp0EcGoTwQ0cCv6Qv9CCGNkONIy44wuItj0+oB2pGUOYvcFAYQQxogDHjS6iGDT6wMafYF/uSq3EMa7xJGWeZjRRQSTXh3QjrTMUcB8o+sQQgCgAfftcy/RpFcHNPoYTIvRRQghmhzrSMs8yugigkWvDWj/2MtzjK5DCNGKTF7poF4b0OgfpTSjixBCtDLLkZZ5rNFFBINeGdCOtMzDgVSj6xBCtOsuowsIBr0yoIH7jS5ACLFXMx1pmScYXUSg63UB7X/TZxldhxBin6QVvQ+9LqCB240uQAjRIYc50jLnGl1EIOtVAe1Iy5wCHGl0HUKIDks3uoBA1qsCGviL0QUIIfbLoY60zBlGFxGoes0kDkdaZjzwR6PrqN/1KwWv3dbmfVEzzyfq8HOp/PFNqtZk4quvwp48ltg512KNTmz3mK4iJ2VfL6Qh5zdQiojJJxI7+7Km+711VZQvW0Tt7z/gc9Vh6+9g4J8eBaAhZxMlnz2BpywP+6DxxKfeiDksSn9cbQV5L1zDgD8+gDUupQt/CkLslz+jL0kq9tBr1oN2pGXeSgCM3vC5G/BWl7TY5i7aSdG7D5B06ZPUbV9F+bevEnvCVVijEylb/hLe6jKSLlmAZm7999JVtJP8l/9G+LhZREw+EZQPb005YSMO9T9fPfmLb8IUEkHUEX/EHBqJq8hJxPhjAMh++lIiDz6VkMETKV++GEtsMrHH/h8AxR8+jCUqkegjz+/mn4oQe9UApDgzUouNLiTQ9IoWtCMt0wJcZXQdACarHVNMUott5UsXET7+aKxxKeS/egtRh53VFKAJp9xM9tMXU7djNWEjprc6XunnTxE6/GDiTry6zeerXLEE5apjwJ8eQbPYALD1HwroLWTlrify4NMA6HfQSVStyQSgzrmWhtzNxM2RXiFhODtwCfBPowsJNL2lD/oPwCCji2iLq2A7tVtXEHX4H/HWVuCrrcA2YHjT/eaIGKyxKTTk/t7qse6yXBqy1hM1/ax2j1/9y2f0m3ZqUzg3ZwqJQHk91Gf/hvJ6qN32E9a4QSiPm9LPnyL2hD+jWaxd80KF6JwrHGmZMvN3D72iBU0AnxysWLGEsBHTscYMRHk9YDLjqSxssY/PVYevtrzVYxtyNoHJjLe6lNzMR/BWl2JPGk3McVdgjU7EU1mEt7oEU0gEBa/fhqtgO5aYgUQfNZ9QxxQ0k5nY2ZdT8PrfwevBNnAE/c9Kp+KHN7AnjiTUMaVnfghC7NswYA7wsdGFBJKgD+hAHlrnqS6ldvN3DDhHXxtGM1sIG3kYlSuWYE8ehzUmiYqVS/BWFoHW+sOMt7oUzWyhYuUSYmZfhoZG2dfPU/hmOkmXLMBbXQpA5cq3iZpxDpboRKrWfEjhW+kkXfok1pgkIibOJmzMTHwNNVgiYnGXZFP9yyckzn+Msq9foGbTt5jDo4g94WrsiSN69OcjxB7+jAR0C72hi6PtztkAUP3Lp1iiEwkZMqlpW+zxV2GJHkjeC1ez6+EzcOVvxZY4EnNYdOsD+LwodwPxJ91A6JDJhAyZRNzcv+ApzaYhdxPK5wUg8tAzCB83C3vSaOLm/gWTPZya35Y1HcZktWOJiAWg5LMniZp5PnXbV+MuyyX5smeIOuwcSj58pFt/FkJ0wEmOtMwhRhcRSII6oP0Xgz3b6DraU7vxG8JGH95imzk8mgHz7iHlutdJuWYxCaffhreyENuAYa0ebwqPBrMFc2RC0zZLrD4czltTjjk8Wt8WM7Dpfs1sxRI1oM0uk+p1X4LHRcTkE6nfsZqIScejWayEjZqBt7YcX31151+0EAfOBFxhdBGBJKgDGn3Fuiiji2iLuyQbd8kuwka2fYUfc0gE5vBoan//AeV1EzL0oFb72JPHgteDK3/r7uMW7wTAGpeCJToRU3i03lftpzwuPOX5TUHeyFtXSfnyF4mdcw2apqG8bvDqLXClfPr3JnOnX7cQnXSRIy0z2HOpywR7H/R5RhfQnvpdv6JZ7dj26Net2/kLGlpTsJYvXUjMMZdgsoYAULnyHRryt5Bw6s3Y4gcTOvwQSjIfJeb4K9A0jdLPnyF02MHYEhwARB16BuXfvd40GqRyxRI0s4WICbNbPG/Z1wsJH39M0+PsKeOoXPk25qj+1G1dgS3BgckW2u0/FyH2YSAwE1hudCGBIGgD2pGWGUkAr/nckPc71ngH2h4n/7wVhZQtexFfQzXW2BRij7+S8HG7F9/zVBbiLslq+j7+lJso/eIZit7WL+UWNnI6scft/hTY75DTUR435UsX4q3TZyb2P+duTPawpn3qs9ZTv+tXki59Yvfjpp6Cq2AHBa/dii1+CHEn39jlPwMhDtA8JKCBIJ5J6EjL/BPwotF1CCG6XAGQ5MxI9RldiNGCua+n/dkbQohgNgBZ0x0I0oB2pGX2A+RqDEL0XmcYXUAgCMqARu97thtdhBCi25xmdAGBIFgDWv66CtG7DXKkZU41ugijBV1A+1eum2N0HUKIbtfnW9FBF9DAwUA/o4sQQnQ7CWijCzgAxxhdgBCiR0x2pGUm7Hu33ksCWggRyGYaXYCRgiqgHWmZVuAIo+sQQvSYgFxKuKcEVUADhwJh+9xLCNFbSEAHEeneEKJvOciRlhlhdBFGkYAWQgQyM3D4PvfqpYImoB1pmTZghtF1CCF6XJ/t5giagAYOA2TBYiH6HgnoIDDd6AKEEIaY7v8E3ecEU0BPNLoAIYQhQtBnEPc5wRTQE4wuQAhhmElGF2CEoAho/0UkxxpdhxDCMH3y9z8oAhoYjv4xRwjRN40xugAjBEtAS/eGEH2bBHQAk4AWom8b5EjLDDe6iJ4mAS2ECAYaMNroInqaBLQQIlj0uW6OgA9o/xKjI42uQwhhuD43kiPgAxoYDFiNLkIIYThpQQeggUYXIIQICBLQAWiA0QUIIQJCstEF9LRgCOhEowsQQgSEaEdaptnoInpSMAS0tKCFEKAPtYszuoieFAwBLS1oIUSjeKML6EnBENDSghZCNJKADjDSghZCNJKADjDSghZCNJKADjAS0EKIRhLQgcKRlhmGrAMthNhNAjqASDgLIZpLMLqAnhToAW03ugAhREDpU2tCS0ALIYKJzCQMIDajCxBCBBQJ6AAiy4wKIZqzGF1ATwr0gNaMLkAIEVD6VAu6T/01EsHrb6FPfrY+fgffhVlH+DQVZnQ9wiDKUgWpRlfRYySgRVDY4Rof/VTRt4fWaFr189GRP78S2S++1mTqc5dA6vM0d5/64xzoXRxCAPCh97CJSlEXrlTEX8oqjlyxM3vsotyCjeMbGr5BqWqj6xM9xmN0AT0p0APaa3QBIjDUYw/NJW5d823TGhrGvp5bcOQPO7PVJeWV34T6fJuMqk/0GAnoAFJpdAEicLzjndnQ1vYIpfrdUFZ+5Mqd2WNeyCv4bWyD6xuUqunp+kSPkIAOIOVGFyACx2LP8aOVQu1tn0PqG8a9kZt/5Pc7s73zKyqXh/h8m3uqPtEjJKADhTMjtQZwG12HCAwFxPavIWRjR/btp1TkTaXlR/20M3v0c3kFG8Y0uL5FqdrurlF0uzqjC+hJAR3QfmVGFyACx1e+gwr39zHT6xvGv5mbP/O7XdnuCyoql9l9vi3dUZvoEQVGF9CTgmGYXTnQ3+giRGBY6JmTdKr5hwN6bKRPRd1SWj7rltJyvgsNWfdwbHTlFqt1KpoW2sVldpqn2kPBmwVUrqnEV+cjZFAIw+8c3mo/d5mb3MW51GyoQbNpxM6Kpf+Z/dE0fY6Xu9xN3it5VK+rRjNrRE6LJPGPiZhD9fkeZcvLKHy3EG+9l5gjYkg8L7HpsdUbqsldnMuIe0ZgsgZMWy7P6AJ6UjAEtLSgRZOf1chRHmXKsWi+5M4c54i6+olH5ORTYTKVPxkdtfKtfhEpLpPWOgEN4Gvw4XzQiSnMxKCrBmHpZ6E+q77Vfsqn2PnoTszhZoamDaWhoIGc53MwR5iJn6Mvm5z1VBbKo3Dc5MDX4CP3xVzyXs4j5bIUGgoayH8jn5TLUjCHmcl+NpvwceFEHhSJz+Uj96VckuYnBVI4A+QbXUBPCqiffDskoEULa9WIbV11rCifL/rW0rJZq3dmDX8yv/DX4S7XdyjVOg17UNFHRXjrvTj+6iBiXAQhg0KIPjy61X5Vv1ZRn11PyuUphA4NJfqwaGKPiaXki5Kmfep21JFwcgJhI8KIGB9B3PFx1O3Qu3Hrd9YTPjacfpP7ETYyjMhpkTTk6QNlij4sImx4GBHjInrkNe+HPtWCloAWQWex5/humU12ZF39pHdz8o/4ZldO3bmVVctsSm3vjufZl7LlZcQdF4fJtvdfz5qNNYQMCsEas3tNsYjxEbiL3bjL9XPrUdOjKP+2HG+tF3e5m/IV5URNjwLAGmulzlmHu9yNt8ZLzaYa7El2GnIbKFtWRuK5AXm9ZmlBB5hyowsQgeVj36ETlaLbZg9G+3wxfy8pm7XamTVsQX7hL8Nc7u9Qqs0x2F3NVeLCU+bBHG5mx4M72Hj1RrbdtY3q31q/XFehC1tCyxV5rXF6WHvK9NFoA88bSH1OPRuv3sjm6zdjsphIOFm/KEljq3rz9ZvZeM1G7Cl2+k3uR86LOfQ/oz+WyIDsAe1TLeiAfAf2UGp0ASKwuLDas1T/nwdrhYd193PNqqufPCsnj1KTqXRBTNSP7/aLGOzWtKHd9Xyecj1Yiz8pJuHkBGwJNkq+KGHnIzsZcd8I7AN2X8PCV+/DGttyRd7GVrfP7UMpRdYTWZjDzDhudqBcirxX88h9KZfki/Uu/OSLkkk8W28pm8PNlH1TBj6IPCiSXY/vonZ7LaGOUJIvScbSz/C4qFk3f12V0UX0pGBoQTuNLkAEnre8R/XohIVYny/2jpKyWWucWUP/XVC01uFyf49Srq5+HuXT5+HEz4kn+rBowoaHkXxpMqZQExU/VrTYV7NqKHfLeTs+tw8Ak91E9bpqqjdUM/jawUSMjaDf5H6k/F8KZcvKqM/d3c1uDjdjDjfrI0eWFJB0URJ5r+UROjyU0f8aja2/jcJ39nt0Y3foU90bEBwBLWNWRSuveGePUQqfEc99bG3dlA9y8g5fuiun8oyq6qUWpXZ21bEbuxVsA3Z3XZgsJmwJNjyVLf8mWaOtuEtbzuNq/N4Wb6M+qx5zP3OLPuqQofp1mBuyW/fY5L+eT/QR0YQkh1C9vprYY2LRzBoxR8VQuyUg5vhIQAcgCWjRSglR8VWE/WZkDXE+X/xdxaVHr3FmDX6koOjnIW73DyjVqZmvtgQblkgLdVt3T5jzuXy4Cl3YE1teojNsVBh1zjo81buDu3p9NaFDQzGH68HsrfK2CPbGYLZEt+yuqNlUQ83mGvqfpk85UG6F8uqtc1+DD80SENfO6FP9zxAEAe3MSM0FZOEb0cpnvoOLja4BQAPt+Nq6gz7MzpvxdVZO+R860arWTBrxc+MpfL+Q8u/LqdtRR/az2WgWjeiZ0eS9nkfe63pORR0ShTnSTM6zOdTtrKP8h3JKvy4l4RT9JGC/qf2wxljJejKL2i21VP9WTfaz2YQMCSFs5O6BMD6Pf8zzhUlNfdhho8IoXFJI3c46it4vImJ8QAy3kxZ0gNpqdAEi8Cz0nJhidA17ivf6Eu7xt6r/VVi8ZtABtKrj5sQRPzee/Dfy2X7fdrzVXhw3OTCHmnEVuHAV6F3fJrsJx18deGu9bL9nOwVvF5B0QRKRUyMBMIeYcaQ5MNlNOB92smvBLkIGheC40dE0WxCg+KNi7El2+k3q17Qt6YIk6rLq2PHADkwhpqbQN1iu0QX0NE2pvS4OFhAcaZlvAmcZXYcIPFvsF2ZZNe8go+vYm0KzufCxmKjfPo4IH+7RtICuNcCdsW7+uneMLmJ/aZpmVkod0Nr2wdKCln5o0abVatQOo2vYl/5eb//79VZ1yoOFxauS3Z4fUapPLZvZRdbtexdjaZr2o6ZpR++xebmmaQd0IUXDBzZ2kHRxiDYt8pwQcZitQyuQGk4D7aSa2oNPqqmlwGwueCQ2etOn4WHDvZoWcF01AagW2K+ZnZqmhQAh+/k8iUBH/kPlKKX2+b5pmnYoMANI0DSteS9ArVLqo309PlgCWlrQok1f+KZNVIpKTSPS6Fr2xwCvd8CDRSUDHigq8X0UHrbqPzHR3jyL+WA0zWx0bQFqw7r56/Z3WGUacOd+PuZ+oHF1w0uAc4ET9tjnIaCjXVU3o/9huaDZtiHo2bvPCU/B0sXxu9EFiMDkwWLdoRI3GF3HgTKB6eSa2oM/y86d/llWbuGc6pqlZqX63MmwDvh1fx+glEpXSml73oAzgQZgYhv3/10pVa/0BbNsgLfxe/82OzAfeKT5c2ma9rGmadXAIcDHmqZVa5p2IjAbcAFzlVLHAacDGvBgR15DUAS0MyO1AMg2ug4RmN7wHh34Z7o7YKDXO/ChopKj1zizEu8tKvkp0eNZyQGeXOqFuqT/WdO0ZOBZ4Bal1Pp97B4FHK5pWlyzbVcAvyulljffUSk1179/KTAXiAGeAC4FVgMPa/q6428CG4BnOlJvUAS034Gt0i56vde9x4xVqvdcAd4EptOqaw75PCv30E+ycwuOr6ldalKqz03S2MOazh5A07Rw4B307oXt2r67kwagt6IvarZtDjBN0zTlvw1odt8MIB64Cr2b5O9KqbeBq4FjgE3ofeLzVAeHz0lAi6BXTr+YciL21RoKSskeb9IjhcVHr3Fm9b+7qGTlAI/nJ5QyZIq7gbzordADpmmaDViC3sL9FDgCuEPTtC81rd0LNQwDFgE3aLuvujMHPXyvQu92ab5IyWn+r2HAe8C7mj7gfB4w0H/fUOBiTdM6NPMnmAL6e6MLEIHrE+8h5UbX0J3MYD69uubQL7JyD/k4Ozdvtt6q7isz6zasm7/ugBcD0TQtGj2UQ9D7gBsXuXoMvet0naZpt2iatuegiYnoQbsUuAVA6Qtkef3f393YEvY/xznoof0w8BV698Za4G/A+YAD+AdwK1Ciadr9+6o9mAJ6DWDolS5E4FrkPXGw0TX0lBSPN/mxwuKjVzuz4u8oLlmR4PGs6uWt6pUH+kBN0yagN+4UcIpSqmmRE6VUmVJqPvokuBuAnzRNm+Z/3Hggyf/Yu4DrGu8DMtCnnb/d7KmuBz5n91XHlwJ/Rg/rcUqpT5XuJfRW9LnoIb5XQRPQzoxUN538mCN6r81q8FCXsgT8pJWuZAHL2VU107/Kyj04Mzsv9xi9Vd0br3r904E8SNO06/yPfQc4XinV5lrS/vHIk9Cnkp/i33wp8J1SqlAptQU9bN/XNO124ELgnD36kacB/2x2zG/8x3oRqNc0zdN4Q2/Bz1NKfbGv1xA0Ae0n3RyiXSt8Y3cZXYNRBns8Kf/RW9VxtxeX/hjv8a4mGNZx6JgDbUEXA2f6h87t9SSyP4hTgbs1TRsMXIneBdLodfTxzPcATyilsvY4xGVKqT2HAyugRCllaX5DHwnSIRLQotdY5D0h2ugajGYBy7yq6sO+zsqZ9mF2Xvas2tqlmlJFRtfVCcUcwBhoAKXUKx2ZrbcHDf3E4Cr0k4pomjYO+Aa9y+OfwK3+cc+HNHuubjkfIAEteo2vfQdN8Cm5yHCjIR7PoAUFxUevdmZF31pc+kOs17smCFvVnxzADMLOuAuYgj4ZZY6maR+jn//6GZiklLoFmIB+rdQfNE3L0jTtyr0cL65594a/i6NDY6AhyALamZFaCGwzug4RmHyYzFtVsqGL+AciK1jPq6qesWxXztT3cvJ2zaytWxZErerMHn6+t4AzlFI7gDHAemCEUupapVQNgFJqq1Lqj+ijMp4Avmv2eA9610ajTnVxBMVyo8050jKfQO+wF6KVS8wff3+HdfHhRtcR6FzgeiOy3+r/RkeGlJlMU1osEB04PEDCuvnryo0uxChB1YL26+m/qCKIvOk9arxSdOqyU32BDWwXVFbNWL4r56B3c/J2Hq63qkuMrmsP3/flcIbgDOiv0JceFKKVKsKjSogM+HWDA8lwt8fxTEHRrFXOrIi/lpR9H+31/mJ0TX4fGl2A0YIuoJ0ZqfV0YIC36LsyvYe1Od5V7J0N7BdVVh3+za6cyUuy83YcVle/TFOq1MCS+vyn5aALaL8+/5dVtG+R94R9rrMr9m6U2z302fzCWaucWeE3lJZ9F+X1HtBQt07YsW7+uj5/wjdYA/o9oDdPbRWdsEMlDa5XVhnt0wVsYL+kouqIb3flTHozJ2/boXqrurwHnrrPt54hSAPamZGaj4yJFnvxnW/CnjO9RCeNcbmHP59fOOunnVkhfykt/66f19edrWoJaII0oP2WGF2ACFyLvCfG7XsvcSDsipDLKiqP+H5X9qT/5eRtnVZXvxylKrrwKWqAr7vweEErmAP67X3vIvqqb30TxvuUVmx0Hb3dOJd7xKL8wqN+2pltu6as/NsIr68rRtB8uW7+uoYuOE7QC9qAdmak7gJWGF2HCEwKk2mTGrTZ6Dr6ihClQq8or5z5w67sia/l5G85qL5TrerXurS4IBa0Ae33vNEFiMD1qne2XCHbABNcrpEv5RUetXJntvWqsopvw32+/bmobxn68qCCIJzq3ZwjLTMCyAM6dPkY0beEU1e93n6pVdOwG11LX/er3bb5n7ExBb/YbVPQtMi97PrEuvnrrumxwgJcULegnRmp1cjHIdGOGkIjioiWWYUBYFKDa/TLeQVHrdiZbb5cb1W3N8b5hR4tLMAFdUD7/dfoAkTges97uCwLEEDClAq/trxi5o87s8ctzs3fNLG+YTlKVfvvXrtu/rpOX727Nwn6gHZmpK5CX6tViFZe8p7Q3hWbhcGmNLjGvJpXcNSPO7P5v/KKb4a73E8YXVOgCfqA9nvW6AJEYMpS/ZNrlU1GcwSwcKUiriurmPZuTt5bRtcSaHpLQL+CPrhdiFa+8U3KM7oGsU+vkV5RbnQRgaZXBLQzI7USeMPoOkRgWug9sX9nj/FLvpdp/61mqdPT5v07ynzY763konfr9nqMExbXEH5/JYn/quLi9+oorm25pEydW3HL5/WkPFKF/d5KRj1eTVWDPtLq9xIvM1+oIeL+Sma+UMP2st2PrfcoRj1ezdc72q4vCDxpdAGBqFcEtJ+cLBRt+tE3bqxXaQUH8tiVOV7OfrOWGc/XsCav/fW5bvuqHtderxsNV2XWc7TDwg+XhvP8qSEsc3q48J3dge5TitNer+XjrR7+e0oIqy8P566j7Zj9v6WXf1DPzMFmvr80nHEJJv72eX3TY+9b3sCMQWaOGWo5kJdptG9Jr5CTg20IynezLc6M1B8daZnfA3K5I7EHTdugHL9P0nYM2N9HvrnBTahF48Pzwpj9UtsDQj7f5uG7XV4OS9n7vJjXzwplcJSetpMGmKlogAverqPapYiwabz0i5sfs71svy6C+DB9vwn9dx/zp1wvH50fRphV4/rDbJz9ph7um4q9PPezm1+vDN/flxcoHjS6gEDVm1rQAPcYXYAITC97jzugySr/PN7OS6eHMiym7V+VGpfiysw6Hj0xhHDr3o/VGM6NQiz61UV9/rliz61xc8lBtqZw3tOgSBMfbPbg9Sne2+RhbLy+35Uf1nPvMXYSwoPy13kdsnJdu4LyHW2PMyP1E+Ano+sQgecD7+ETlKL9DuJ27Otaqtd9Us/YeDNnjttHOu9BKcXzP7uZNtBEpF3D41P8lOtlSJTGGf+rJfbBSiY/Xc3r63dfXvHfc0K44sM6rPdUsXCtmwdm21n4swuvgksO2r/nDyD/JL0ieKczd7NeFdB+0ooWrdRhD8sjtktnFf5vvZv3N3t47tSQ/Xqc26u4/IN6vtrh4cnUUABKahUuLzy+0sWcERY+uzCc44dZ+OOSOpbv1E/8nTjCQuHf+rHrhgg2XxNOTKjG379q4OnUEP71vYsR/6liwpPVfLYtaE4UOoHXjS4ikPW6gHZmpH6ATFwRbXjHO7PLlrD8Jd/LZR/U8coZoSRGdPzXKLvSx9Ev1vLhFg9fzw/j0GS9j9njP/84f7KNy6fZODjJzL9OCGHSABMvrt3diraZNVIiTWiaxt8+b+CiKVbyqxVLNnpYc0UEi/4Qyvx366hzB0Wj9GHSK4Lmr4kRel1A+0krWrTysuf4kUrRJcn12AoX1S445bVaQu6tJOTeSr52eln8q5uQeyubWr3N/V7iZfpzNfSzwS9XhnNYyu5z9HFhGmYNRsS2/JUcE2+ioKZ1ycucHpbv9PCPo+x8stXD+ROtRNo1Dk4ykxKpsbkk4K8IV4SsRrlPvWYUxx7eRT/5MNHgOkQAySMusYaQjRHUj+3sse4/1s4tR9habLvkvXoGRGg8MNve6oQgwHlL6pg1xMzLZ4Ri2qNvO8SiMXWgiR+yPZw/Se9PVkqxodDHicNb/pq6vIorM+t54qRQQq0aDV5w+3aHeK0brIHf9LqP9Ir9PifQ1wT+23gAnBmpCrjX6DpE4PnaN+WAxkPvaWA/E2PizS1uYVaIsmv+f2u8vt7NCYtrcHkVW0q8rM7zccZYK9vLFFtLfU23ino9XG8+ws6za9w8vsLFz3le/vJxPTvKfVw7veUfgge/dTFpgIk5I/TgnjnYzNOr3PyQ5eGZVS5q3YpRcQH9q70DeMroIoJBb21BA7wFbAQ63VoSvcdCz5zEU8w/9shz5Vb52Fjsw+2FvGo9hBvHLjf36Il2rj/MzlnjrJTVKR74toGbPldMGmDik/PDcETvDtutpT4W/ORi7RW7xzyfNc7Ct7u8nPRqLSmRJv53VhhW895HnxjsH6RXuIwuIhgE9YL9++JIy/wDcnUGsYet9gvyLJpvoNF19FFrgakytK5jAvpzUGc5M1LfBT4zug4RWH5Vw7YaXUMfdquEc8f16oD2+wvg3udeos94yXNCqNE19FFfkV7xidFFBJNeH9DOjNTNwGNG1yECx0e+6ROVkuVpe5gX+KvRRQSbXh/QfvcAuUYXIQKDC6s9W8WvN7qOPmYB6RVrjS4i2PSJgHZmpFYBNxtdhwgcS3xHSbdXz8kF/mF0EcGoTwQ0gDMj9RXgG6PrEIHhZc9xo5Ui4Kfb9RI3kF5RZXQRwajPBLTfteh9YaKPKyY6oYrQ34yuow/4jPQKudrRAepTAe3MSP0FeNroOkRg+MI3rdjoGnq5euBqo4sIZn0qoP1uBXYaXYQw3iLPiclG19DL3Ut6hYw574Q+F9D+E4YXQdesaiaC169q+Ei3MmcbXUcv9SOQYXQRwa7PBTSAMyN1KfC40XUI461RI7cZXUMvVAv8ifQKOd/TSX0yoP3SgM1GFyGM9ZLnhAija+iFbiK9YovRRfQGvXqxpH1xpGUeDHwPBO0F3UTnWPG4frf/qV7TiDS6ll7iY9IrTjK6iN6iL7egcWakrgJuN7oOYRw3FptTDdhgdB29RAlwqdFF9CZ9OqD9HgK+MLoIYZw3vUfLhJWucRnpFXlGF9Gb9PmA9l995U+AjInto17zHjNWKZnA1EkPkV4ha693sT4f0ADOjNQ84AJklmGfVEZkbAXh0s1x4L5Gn18gupgEtJ8zI/VTZEGlPusT7yGlRtcQpHKAc2VIXfeQgG7GmZH6CLDQ6DpEz1vknTPY6BqCkBs4m/SKQqML6a0koFu7EvjO6CJEz9qkBg9zKbMsAbB/biS94geji+jNJKD34MxIdQFnALuMrkX0rJ98Y5xG1xBEnie9YoHRRfR2EtBtcGakFgKnglwWqS9Z5D0xyugagsSn6J80RTeTgG6Hf2nSC5FFlfqMr3wHTVCKCqPrCHC/oPc7e4wupC+QgN4LZ0bqO8CdRtcheoYXs2WbSpLhdu3LAk6Sq6P0HAnofXBmpN4DPGt0HaJnvOY91ugSAlUlkEp6hVx8uQdJQHfMlcBLRhchut+b3qPGK4V8fG/JBZxJesU6owvpaySgO8CZkeoDLgH+Z3QtontVEhFVSj8Jot0axzrLejUGkIDuIGdGqhd9Ovi7BpciutlH3umVRtcQIDzoswTfN7qQvkoCej84M1I9wDzgI6NrEd1noXeOw+gaAoAXuJD0ireNLqQvk4DeT/6JLGcCXxpdi+ge21XSkAZl7cuXwvIBF5Ne8brRhfR1EtAHwJmRWo8+keUbo2sR3eN737gso2swiEJf13mx0YUICegD5sxIrQVSgeVG1yK63iLviXFG12AAD3q3xgtGFyJ0EtCd4MxIrQJOAN4wuhbRtZb7Jo33Ka3E6Dp6UD1wOukVrxhdiNhNArqTnBmpDcC5wCNG1yK6jsJk+l2lbDS6jh5SAZxIesWHRhciWpKA7gLOjFTlzEj9K3A9+gkW0Qu86j3WbHQNPSAHOJL0ih7vqtM0rS/8fDtFAroLOTNS/w2cg/5xUQS5Jd6jJiiFy+g6utFG4PCemCGoadqPmqYdvcfm5ZqmpXb3cwczCegu5sxIXQIcD8gllIJcDaH9iojqrbMKPwJmkF7R4XXPNU0bo2ma6sAtuwPHOhSYASRomnZWs9tJnXhNvY4EdDdwZqR+CxwBOA0uRXTSB97Dq42uoRv8EziF9Ir9XVp1MxDqv12NPsw0dI/bAmBVB451M7AdfXbulf7bA8AT+1lTryYB3U2cGambgMOQYXhB7UXvCcONrqEL1QHnk15xC+kV+32uROnqlVL1gA3wNn7v32YH5tPshLmmaR9rmlYNHAJ8rGlataZpJwKz0RdhmquUOg44HdCABzv7InsTCehu5MxILUD/j/gvo2sRB2aXGpBSp2xbjK6jC+QAR5Fe8WoXHS8KOFzTtObjxa8AfldKNTVKlFJz/fuWAnOBGPRW8qXAauBhTdNCgTeBDcAzXVRfryAB3c2cGakeZ0bq39Cnh8siPEHoG9/EHKNr6KRlwMGkV3Sk66GjBqC3oi9qtm0OMK1ZX/QA//YZQDxwFXo3yN+VUm+jd5McA2wCQoB5Sim5glEzEtA9xJmR+jZwMPCz0bWI/bPIe2KC0TUcIC9wB3As6RX5XXzsYcAi4AZ/Cxj0gA5FD+JfgUL/9tP8X8OA94B3NU3T0BceG+i/byhwsaZpEV1cZ1DT5A9Wz3KkZdrRT9L8xehaREcptc1+QbFZU8EU1LuA80iv+K47Dq5pWg56C/gsYKtSKt2/3Qr8DtyklFqiaVo0+nUMy4HrgCOBEvTukFDgWuAz9Ot/3oveMn9YKXVbd9QdbCSgDeJIyzwVeAHoi2s+BJ0Pbbd9O8HknGl0HR20BPg/0ivKu+PgmqaNB9ajh2kUsBI4Tim1WtO0h4HDgcOVUkrTtHQgBZgApKG36p9Cb6S8qpTyNDuuGX0RsiqllFwgAOniMIwzI/V9YAqytnRQeNl7nNXoGjqgBriS9Iqzuiuc/S4FvlNKFSqltgB/Bt7XNO129JbwOc36kqehhzEASqlvgFOAF4F6TdM8jTf0UR3zJJx3k4A2kDMjNduZkZoKnMfu/joRgN7zHjFRqYCeIfo5MIH0im4dBaFp2mD0McuPNdv8OvqY5nuAJ5RSzZdqvUwp9fseh1FAiVLK0vyG3u0hmpGADgDOjNTXgLHoJ11EAKrDHlZATCDOKiwFLiK94gTSK5zd+UT+LohF6BNRlvi3jUOfsJKE3lK+1T/2+RAApVRXn5zsUySgA4QzI7XUmZF6MXAc0Jev5hGw3vHODLQW9BvAWNIrXuyh57sLvVtuPjBH07SPgTXoI5MmKaVuQe9rLgd+0DQtS9O0K9s5Vlzz7g1/F4eMgd6DBHSAcWakfglMRJ9R5dnH7qIHLfYcP8LoGvyygNNIr5hHekVPdo29BZyhlNoBjEE/UThCKXWtUqoGQCm1VSn1R8CBPiGlcRSJB71ro5F0cXSAjOIIYI60zCnAf9GnyYoAsMF+8cZwrWGsQU9fA2QAD5NeUWdQDaIHSQs6gDkzUtcC04E/AluNrUYALPVNKTDgaRX6qIdRpFfcK+Hcd0gLOkg40jItwP+hzwwbuI/dRTc5RNu06U373WN68Cm/Ba4nvWJ1Dz6nCBAS0EHGkZYZhj4j62Yg2thq+qZt9gvyzJqvu/9I/grcRXrF2938PCKASRdHkHFmpNY6M1IfQF8L4Z/oS0iKHvSrGtqdq9utBc4Apkg4C2lBBzlHWmYScDv6qmKhe99bdIUzTct/etj2dFefuP0ZfRjb+6RXyC+lACSgew1HWmYccDn6AjbJBpfTq9lx1W+yX+TTNMK64HDfAf8kveL9LjiW6GUkoHsZ/8nEs9CvMD7d2Gp6r2/tf1mRohUf6M+3AX169H9Ir1jThWWJXkYCuhdzpGVORz+heBYQDIv9BI0bLG9+c53lnSP382E56Cu5/Zf0iqJuKEv0MhLQfYAjLTMZfcWxi5Ehel1iAKWFP9qvSdA0tH3s6kVfyGgh8DbpFTI7VHSYBHQf4kjLNKFfI/EC9JECcvWKTlhnv3RDP61ufDt3/wwsBl7rhquZiD5CArqP8o+nPg04B/1SRSHGVhR8HrU+sfR083dHN9uUDbwKLCa9Yr0xVYneRAJa4EjL7AecDJzN7uvKiX2YrG39/T37HV7gXfRr7a2UIXKiK0lAixYcaZmhwEz0rpDZwFRkQlNz9cBS9CvhfOTMSJWlYUW3kYAWe+VIy4wGjgaORQ/scUbWY4BaYDXwI3owf+3MSJXZm6JHSECL/eJIy0xED+tZwGT0BdrDDS2q6yj0K1KvQA/kH4F1zoxUGXkhDCEBLTrFkZapoa8LMgn9QgONX0cQ2F0jlehXrtkGbEAP4xXOjNQyQ6sSohkJaNEt/KNExgHj0aeeD2x2S/J/tXdzGQXsDuFt6GtqbwO2OTNSZaKICHgS0MIwjrTMGHYHdiJ6V0kI+iiSEP/Ngt4S15p9rQMq2rmV+79WOjNSfT33aoToehLQQggRoAK5j1AIIfo0CWghhAhQEtBCCBGgJKCFECJASUALIUSAkoAWQogAJQEthBABSgJaCCEClAS0EEIEKAloIYQIUBLQQggRoCSghRAiQElACyFEgJKAFkKIACUBLYQQAUoCWgghApQEtBBCBCgJaCGECFAS0EIIEaAkoIUQIkBJQAshRICSgBZCiAAlAS2EEAFKAloIIQKUBLQQQgQoCWghhAhQEtBCCBGgJKCFECJASUALIUSAkoAWQogAZTHiSVevXm0zmUxXmc3mi5VSUYBmRB1CiF5JaZpW4fV6F/p8vqemTZvmMrqgA2VIQFsslmcjIyOPSEpKqrHZbCWaJvkshOgaSilcLpc1Nzf32srKyqnAfKNrOlBGdXHMHDJkSIXdbndLOAshupKmadjtdveQIUMqgJlG19MZRgW02WQyKYOeWwjRB/gzxmx0HZ0hJwlFn+PxeIwuQeynvvqeSUCLXm3y5MljPvzww37Ntx166KFjXn/99SijahJ7J+/ZboacJAx2M2bMGHXNNdcUnH/++RUANptt6vr169dXV1ebvvjii34333xzUUePVVdXp1VUVJhKS0vNCQkJ3oSEBG/3VR6cfv7555CpU6eO39d+/fv3dxcUFPy6t32+/vrrsLVr14YXFhZaFi5cGNO4PSwszDdv3ryKrqi3N6mtrdVqa2v3qyGXlZVl7ar3C/r2exYwAe1Iy5zWk8/nzEhdfSCP27Jli23lypX9pk+fvqNxm8/nQ9M0bDabWrBgwYDPPvsscsmSJTuioqJ8Z511lmPt2rXhHo9H83q9uN1uk9vt1nw+H1arVVmtVhUaGuoLCQnxXXXVVQVXX311ade9yv2QHtWjP3/SKzr88588eXJ9TU3NGoD//Oc/8W+99Vbs8uXLf2++z1VXXZWSk5Nj29exHnzwwcSUlJSG119/Pa5xW05Ojs3j8Wjz5s1btz8voatMfHFij/7s181f1+Gf/e233z7w0UcfHbg/x7/mmmvyu+r9gsB8z3pKwAR0sHj88cfjDz300KoRI0a4G7f5fD7NarWqkSNHun788cdNJ5988ohVq1aFzp49u+app57Kcrvdmt1uV3a73RcaGqquuuqqlIiICN8jjzySa+RrCRYmk4mwsDAF4HK5NLPZrBq/BygpKTEvWbIk/s0339zSuO2oo44auWrVqoiGhgbTWWedNdJkMqnFixdv+/777yMTEhLcX3/99Ra73a7KyspMkydPHnfdddflG/HaAt0jjzyS29b/0xdffDH68ssvH/btt9/+dsghh9S39/iOvl8g71lbJKD3Q1VVlenll19OePTRR3c2bmtoaNCUUoSEhPgAkpKSPKtWrdpkMplYtGhR9E033TRkz+PU1NSYNE3jpZdeStjzvgceeGDXZZddVta9ryR4VVRUmH/++eeI/Px8c2Jiohfg0UcfjXc4HPVz586tbtxv+fLlWzweDwkJCZMXL168/fjjj68eOXLk+Mcff9z5zjvvxFxxxRUpCxYsyD711FOHjxw5su6mm27qcLdUX7djxw7rdddd57j99tuz9xbO0PH3C+Q9a4ucJNwP9913X/+ysjLLmWeeWaFp2jRN06aFhIRMBRg4cOCUxm1ms3mapmnTVq5cGV5cXPxL4+3VV1/dGhoa6h0xYkT94MGDG2JjY91ffvnlpub7SDjvXUFBgdXtdmtPPfVUfOO2L774ImrDhg1hjT//rKwsC8CXX34ZUV5ebnnqqacSampqtH/84x858+fPL3/hhRd2fv/99/1GjRo1oaGhwfTBBx9sN5nkV6EjKisrTaeeeuoIr9fL8OHDG/Y1umJ/3i+Q92xPvf8VdpFt27ZZn3jiiUTQP3IrpVYrpVavWbNmQ0hIiO+ll17aNn78+NrG7Uqp1U8++WQOQEVFhemqq65KvuKKKxwvv/zy9hkzZlSddNJJ5ffee2/2SSedNOqvf/3rwNLSUnkvOmDnzp32M888s+Tpp58eUF1drQEsXbp0S01NzZoHH3xw16hRo+qSk5M9AG+//XY0QF1dnWnu3LkjLrjggnKfz8fChQtji4qKrADZ2dm2xx9/PL6iokJ+/vtQX1+vpaamDo+OjvYceeSRld9++23E3/72t6QZM2aM2rBhg72tx+zP+wXynu2p17/ArnLDDTekzJw5s3LP7du3b7clJia2Odc/NzfXcuuttyaOHj16AsDatWs3zpo1q7bx/nPOOady1apVGwsLC63Dhw+f+Mc//nHIJ598EtF9ryL4bd68OfS0004rP+yww6ruuOOOgQAhISHKbDbz73//O/HWW2/NNZlMFBcXmz/44IOYUaNG1d14440FRx11VNW///3v+LFjx457/PHHE5977rkd2dnZ62677bbcxx57LDEhIWHKNddck2z06wtUxcXF5lmzZo1saGgwffzxx1utVqsCuO222woGDhzoOvjgg8f9/e9/T3S73S0e19H3q/E55D1rSQK6g2bOnFm9ePHinXtuX716ddigQYPaDOjnnnsutri42HLxxRcXlpaWWi6//PJB8+bNG1JQUGDdtm2bfd68eUOuv/765FtuuaXg559//m3IkCEN9fX1Mve9HatWrQopKiqyHnvssdX33ntv7nPPPdf/m2++CQO45pprkhMSEtx/+tOfygHuv//+AUceeWRl47mB2bNnVz3//PP9r7322oItW7asP/PMMytNJhPXXHNNSVZW1rrnnntu+3HHHdfqD7CAn376KWT69OljNE3j008/3RIREdF0wi8hIcH79ttvOxctWrT9mWeeGTBp0qRxje/J/rxfIO9ZW+QkYQe1N7b5/fffjzn77LNL2rrvjjvuKAS9JV1aWmr+29/+ljxmzJj6Sy+9tATgnXfeiXr++ef7JyYmZsfHx3szMjJ6/Vnpznj66afjp06dWp2cnOxJTk72PPTQQ7vOPPPMERdddFHRkiVL4lauXPlbY2ts7dq1YY899ljWhRdeOBRgzpw51R988MGWsWPHTrz++usdzY+rlGLu3LllH3744faef1WB7Z577ul/3333pVx22WUFjz76aI7F0nZkzJs3r2LmzJkbzj//fMc777wTfeSRR9buz/sF8p61RVrQnfDVV1+Fb9iwIeyss85qNVj+6aefjr3hhhuSQB/ZMWHChIasrCz7rFmzqiZMmNAwYcKEhiVLlsQWFBRYR44cOSEmJmby999/H9rzryI4bNmyxfbKK68kXHPNNYWN2y677LLSQYMGNTz00ENJF198cWHzoY+LFi1yTpo0qaH5MTRNIzo62uPxeFY3vz300EOtPhkJXXx8vOfFF1/c9vjjj7cbzo2Sk5M9S5cu3frQQw/l7u/7BfKetUUC+gD5fD6uu+66wccee2z5+PHjG/a8Pysry7Z169amEycrV64MdTqdITNnzmzqg167du2murq6n8vKyn6x2WwqIiLC11P1BxOPx8OFF17oGD9+fO38+fPLAFavXh1yyCGHjC4sLLReeeWV+QsWLBh41FFHjVy2bFkYwODBg/vm4g1d7Kqrrird39l6Sqn9fr9A3rO2BEwXx4HO7DPKlVdembJx48awF198cQeA3W73FRcXW8vKykxms5mvv/6634wZM6pBP/v9l7/8ZdC8efOK4+Pj25zKXVlZaW7vvh6xHzP7etqNN96YtHHjxrAVK1b89tZbb0UuWLBgwIoVK/qde+65RY8//nhOZGSk7+qrry6+9dZbk2bPnj02Pj7efeONN+a11S1VXl5usVgsLWbuNX5c7rlX1NL+zOwLBl35fkFgvmc9JWACOpgsXLgw5tlnnx1w880350ydOrUe4KijjqoJCQnxxcbGHgSQlJTkeuGFF3bm5uZazjvvPEdVVZX5oYceymk8Rn19vVZWVmYOCQnxZWZmRtpsNpWYmCgtiDace+65ZSeccELVmDFjXP/73/+ix44dW7dw4ULn8OHDmz4iT5gwoeGDDz7YsW3btuznn38+7uijj64GsFgsStO0ppNa0dHRnrKysl+aH/+RRx6J/+qrryJ77hX1bp15v0Des+Y0pXp+WeZffvnFOXny5OIef+IuoGnatC1btvx61113DVy8ePGuPe9vHOsZERGhVq1aFXLMMceMOeSQQ6pfeeUV58CBA5sCePPmzbaxY8dOVEoRHR3tufvuu7OvvfbaNk82CiEOzC+//BI/efJkh9F1HCgJ6G721VdfhR977LE17d3v8/noCzOihDBCsAe0JEM321s4AxLOQoh2SToIIUSAkoAWQogAZVRA+3w+n0xpFkJ0G3/GBPXcAkMCWtO0/Lq6uhAjnlsI0TfU1dWFaJoW1MsnGBLQHo/nLqfTaaupqQmVlrQQoiv5fD6tpqYm1Ol02jwez11G19MZhgyzA1izZs2JFovlTqVUItIXLoToOj5N0/I9Hs9dU6dO/dToYjrDsIAWQgixd9JyFUKIACUBLYQQAUoCWgghApQEtBBCBCgJaCGECFD/D7r8ERTDLw0qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "run = plt.pie(male_run1000,autopct='%0.2f%%',labels=['优秀','及格','不及格'])\n",
    "plt.title('  男子1000米跑分数统计结果  ',backgroundcolor='#CD853F',color='white')\n",
    "plt.legend(['优秀','及格','不及格'],ncol=3,loc='lower center', bbox_to_anchor=(0.5, -0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "defd1b90",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1705fcc7b50>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "pullup = plt.pie(male_pullUp,autopct='%0.2f%%',labels=['优秀','及格','不及格'])\n",
    "plt.title('  男子引体分数统计结果  ',backgroundcolor='#CD853F',color='white')\n",
    "plt.legend(['优秀','及格','不及格'],ncol=3,loc='lower center', bbox_to_anchor=(0.5, -0.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03fc1c9",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 女800米跑、女跳远进行直方图绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b2da43",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleScore = pd.read_excel('./体测分数_女生.xls')\n",
    "femaleScore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "da065ce8",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '   女800米跑分数统计结果   ')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_run800 = femaleScore['女800米跑分数']\n",
    "plt.figure(figsize=(9,6))\n",
    "count,bins,fig = plt.hist(female_run800,bins=4,rwidth=0.8)\n",
    "labels = ['不及格','及格','中等','优秀']\n",
    "_ = plt.xticks(np.arange(0,101,step=25),fontsize=14)\n",
    "_ = plt.yticks(np.arange(0,500,step=50),fontsize=14)\n",
    "\n",
    "for i,bar in enumerate(fig):    \n",
    "    h = bar.get_height()\n",
    "    w = bar.get_width()\n",
    "    plt.text(x = bar.get_x() + w/2 ,\n",
    "             y = h + 10,s =labels[i] ,ha = 'center')\n",
    "    plt.text(x = bar.get_x() + w/2 ,y=h/2-10,s=int(count[i]),ha='center',color='white')\n",
    "plt.grid(axis='y',linestyle='--',alpha=0.7)\n",
    "plt.title('   女800米跑分数统计结果   ',backgroundcolor='#CD853F',color='white',pad=20,fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4f4af99f",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '   女跳远分数统计结果   ')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rTjj+0u5l5DsqPu53Pkti3lzGz7mI5ur13Suntn30Mlvf33lZeH9BsMhwpKBEZC/avvpfbF/9L+InzWbcMV9kyhfvZEdFER3N9YxKm8a6R6/d4+eGbH7jQbYVvExz1XqyF95M5dvv7rRNy9YNlL96DxYeiWvtZ3mrz0/6Sd8iYfLRlDx1S/cIAkBn6w5iMw4hauxEopIyiYgbS3NVcb99STp0AS01Zf0+Nr/rKaZ9l8N2tjQSkzqFuMwZVK14ivb6agLtzYxKzycxby7Fj9/Ya/ttBS+RdsLlbP3geTqCc2u8Yyxjw7OLaK3ZSEtN2U6/iGMm5NG6fQtugPkyvoiYAR+DDxAxejzx2bPY+t6SAbfpqb2xho9+c/6A5a6zc8C5KJ2tO7qDEtfR2ns7n5/xcy7ylhs/u4iosVmMnjKHDU//iMlfugvMqHjtdwMep8hwphfyiewD9euXs/aRb1P6/O3EjJ9CQs5sOprr8EfH73Fb/ogYmivXMnrKsYSPGtP9JNO+4ifNZtpX/9CdDo/teqS59/Cz5JlnUf7KPWxf/UavetX/eYqYcZOZdvmDTDzrJmo+XNpr4myXUemHEDdxZvfS2p36GRUH0Ov2UpeGDe+y/m8/IC7rMPK/9RjpJ3+LnM8vovKdx3q9Zyc6JZfUoy/ABToZc/BJ3RNYAXCdbPtwKTvKP8L84cTnHEXqnIuIGT8VfH7GHHwyDSUr+u0bQPrJ32LSuT/cKd98fsYd9T9M++ofulc97Sl/ZCzxk2Yz/riLSdnNywB3JTbjUPIuvoexM05n7V+uofajl7vLmqvXs/qhK4ifdATTLn+QMYd+BvOHf+J9iYQijZSI7APR43JJnnUOSYecSmvtZir//Qhj8k9m0tk/oHlOCZvfeLDXU0/Bm6Pgj4gmftIssNn4o2I59P8tIdDeQuF9F5E2/2s0bir470RX53rVj4gb271qJyH3aCYcfynm8zP+uIvY8ubDbCt4qd+AZttHL7Hto5d2eTxRSZlkn30LzdUlVA/wkruYcbm7fDLt9o9fo7H0fXK+sLj7F3dYTAKRiWm01pYTNXYiuecvpqO5nuKHryR74feZdO6PKfn7j6hf9xYJU+aQdMhniBk/lYj4ZDqaG9hR8THbi14n/cRvEh6fTNV/nuixx97fT1h0QvDR9sH0qEQAss68EV+49/C5yj24tRY38XASDzqBUekHEzU2CzMf7Tu2UfnW4Fcq/betmaTM/jwJuUexreAl1j363X4nI7du28jqP3yD8XMvJXPBNaSd8DXq1iyjbu2/qdvFg+1EhgsFJSJ7hRE9LpeE3KMYnTeXmHG5tDdu81aprPgbrqOVmg9fIG7i4UyYexmTzrmV5qr1bF72UHdwMu7oC4hOnYw/Op7mymK2vPkwzVXFtG7fzKRzfkB47Nhey0U7musJj0tmVNo0OtuaSZgyh+bKdcRmHUb2ObeyrfBV6tb9m+yz/peY1CnUfPgC0cmT6GxvwQU6cJ0d3nt5zI/5w/D5wzF/GOYLwwU6aQk+H2RM/smkn3wlna07WP/4TbhAB2ExCYxKO5j2HdtxHa1EJWWSPOsctgdXxPT6ZvzhxGbOYPTUOYzJP4XO5nrWP3UL/ogYxh9/Cf7IUWxf/QaZp32XzpYG1v7lGtq2b2bdo9cx5Uu/IPfzP6X63b/TULKSQHszm5c9xI6NH9JSU0Z47FjST7mS0VOPo+yFO7r7DNDRtJ3ocblEJWfjj4olNuOQXvMxEqfNB7xf9KXPLtrtfJy+olMmEZt5KI0bV1H1zmM0blrVPW9l/NzLiBqTtsvJqY0bVwFeADnpc7fRVFHE2j9/Z6eXCPYVaG+m/OW72bryaZKPOJcxB5/EjoqdR7ZEhiNzff7aEhnp3v3JvL36H33cxJlkn30rYdFxuECn9xbdD5eyvehfA66ySZw2nwnzLidydCpl/7iLrSufxh+dgOts32new6Rzf0j8pNmUPP3D7qebgjdHIudztxEzPg8zo2VrKev/divRKZNIPnwh6/56PQQ6iR6XS8rszxObnk/YqETv6aK+XT9Do3rl02x6+TdM+fIvGDXhILavWUbZ8//X/dd72KgxHPyNP3W31dm6g/r1yylbemevZ28ApB77ZSbMvYwdFUVsfW8J21b9o/ux7uaPICIhhakX3k1z9XpKnv5hr3kk/qg4Jp51E+0NWyl74fZe7UYkpDLt8gfpaGmk7IU7dnoEfvS4yWSf/QOixqThXICWraUUP35j97t7wuNTSDzoBKqWP95rjk1PB3/zURrL3h9wifdAxs+9jDH5J+92ouuah68EvPcD9Qyo+rY1esocPr7/kn7L8fkh8Ole+jiQmTe+ZvukYZEBKCiRA87eDkowH+knXUHT5iLqipfv9DbXAav5IxhzyMnUfPDCgL8UwVtK7AuL3OVS3758EdE7rYLps/PgqIgfMx8OF7wd5H013nLaTuImHo4LdNBY9sEu29rVm5B9ETGERcf3Ozm2S2RiGq3bNw/4PZg/ot8ALzbjUHZsXq1Jn/uIghIZagpK5ICz14MSkRFKQYkMNa2+ERERkZCgoERERERCgoISERERCQkKSuRAVLm/OyAyDOg6kSGnia4iIiISEjRSIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIWGPghIzu8DMnJldHEz7zSwQzOv6tPSpk2dmr5pZk5ltNLOr9mL/RUREZIQIG+yGZhYJ3NYnOxEwYB5QHswL9KgTB7wMLAOOBo4Dfmlmm5xzT33ybouIiMhIM+igBPg+8B6Q0SNvTPDnu865hn7qXAREA5c455qBD8zsROBKQEGJiIiIdBvU7RszOxS4Ari6T9EYoG2AgARgPvBaMCDp8hJwlJnp7ZMiIiLSbbdBiZlFAX8CfuycK+tTnAREmFmzmZWa2Z/NbGKP8hygpE+dMiAqWFdEREQEGNztmzuBGuDn/ZS9A8wGWoE84Bbgn2Z2aHD0JBbY0adOU/BnVH87M7PLgcsBYmJiDp84cSIA4eHh+Hw+WltbAfD7/URERNDc3NxVj6ioKFpbWwkEvGktkZGRdHZ20tHR0d2GmdHW1jboNqKioujo6NhlG+Hh4bS0tPRqo6Wlha5H+EdFRdHe3k5nZycAEREROOdob28HICwsjLCwsO42fD4fkZGRvdqIjo6mra1tl234/f7u72cwbURGRhIIBHbbRtf309XG7r5jnSedJ50nnSedJ52n/r7jwsLCrc65ZAawy6DEzL4CnAPMcM4F+pY757YCW4PJD83sbWADcAbwF7xgJbJPta5gpG+w0tXmfcB9ALNmzXIrVqzYVRdFRERkmDCz0l2V7+72zU14t1lKzKwluNzXD9zfd+kvQPD2Tg2QFcwqp/fEWILp7c652kH0X0RERA4Qu7t9cxIQ3ievAG8lzt/6bmxmk4CxwLpg1jLga2YW7pxrD+adgrdMWERERKTbLoMS51xx37zgopnNzrkiM7sk2MZyvBGQRcBHwDPBze8HrgHuNbM78Z5ncgZw7F7qv4iIiIwQe/Kckv7UAbcDE4AtwPPATc65NgDnXIWZnQ78CliBN4JyrnNOE0VERESklz1+941zLsw592Dw30855yY556KccxOdc9/sO1fEOfeGc26Gcy7SOXewc+7ZvdR3EZGQdPHFF/Pd7353l9vcfvvt5Ofn75R/zTXXcOaZZ+6rromENL2QT0RkiNXU1HDbbbdx/vnn98qvr6/ngQce4JxzztlPPRPZvxSUiIgMoY6ODr785S8TCAR4+OGHycvL6/4cd9xxOOdYtGhRr/ybb755f3dbZEh82jklIiIHtIkTJ1Ja2v+jF+64445e6aysLM4880yWL1/Om2++ybJly1i2bBkPPPAAy5cv58orr+Ttt9/mtdde44033uDBBx8kIiJiKA5DJCQoKBER+ZRefvll5s6d252+7LLLGDt2LIsXL+7Oe/3117nssss477zzuOqqq5g8eTL5+fmkpKQQERHBAw88wMUXX8xBBx3EQQcdxIQJEwgP7/tEBpGRTUGJiMin5Pf7CQv77/9OzQwz65Xn9/sBmDt3Lueccw7Lly/vLrvqqquora3l6aef5vbbb+/Ov+KKK5g+fTrPPffcEByFyP6noEREZIg99dRTO+VdfPHFzJgxg6uvvnroOyQSIhSUiIh8Sj1frAbgnMM51yuv62Vln//85/nXv/7VbztLly5l0aJFO+Ufc8wx/QYyIiONdb3lLxTphXwiEup2NdG1r6ysLDZs2LDb7R566CGmT5/OjBkzPl3nREKMma10zs0aqFwjJSIin8ITTzzB5MmTSUhI6M67+OKLGTt2bK/5IXV1daxdu5Z//etfu30OSX19PZGRkURGei9Zf+yxx5g/f/6+OQCREKKgRETkU5g1a8A/+npJSEjo3nbr1q0APProo7S1tXHhhRf22vaMM87g/PPP50tf+tLe7axIiFNQIiKyn2RkZLBgwQLi4uI4++yz93d3RPY7PdFVRGQ/OfbYY/n973/PsmXL2LhxI5s2bWLdunV8+OGHjB49en93T2TIKSgREdmPzjvvPO644w5+8pOfkJmZSV5eHjk5OZx00kn7u2siQ06rb0RERGRI7G71jUZKREREJCQoKBEREZGQoKBEREREQoKWBIvIsDPxBr2gbn/asOj0/d0FGaE0UiIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEhD0KSszsAjNzZnZxj7yzzKzAzFrM7EMzm9+nTp6ZvWpmTWa20cyu2kt9FxERkRFk0EGJmUUCt/XJmw08CTwCHAH8B3jGzDKC5XHAy0AVcDSwGLjLzM7ZK70XERGREWNPRkq+D7wHdPbIuw542Tm3yDm3Cvg6sAO4NFh+ERANXOKc+8A592vg78CVn7rnIiIiMqIMKigxs0OBK4Cr+xTNB57vSjjn2oHX8UZFuspfc84196jzEnCUmdkn7LOIiIiMQLsNSswsCvgT8GPnXFmP/EQgESjpU6UMSA/+O2eA8igg6RP2WUREREagwbz75k6gBvh5n/zY4M8dffKb8IKOrm36K6fHNr2Y2eXA5QDp6emsWrUKgNTUVKKjoykp8WKc+Ph4MjMzKSgoAMDv9zNt2jSKi4tpavJ2kZubS11dHdXV1QBMmDCB8PBwSktLAUhISCAtLY3CwkIAwsPDycvLY+3atbS0tAAwZcoUampqqKmpASAtLQ2fz8fGjRsBSExMZNy4cRQVFQEQERHB1KlTWb16NW1tbQDk5eVRWVlJbW0tABkZGQQCAcrLywFISkoiKSmJNWvWeF9MVBSTJ0+mqKiI9vZ2AKZNm0Z5eTl1dXUAZGVl0d7eTkVFBQDJyckkJCSwbt06AGJiYsjJyaGwsJDOTu+OW35+PmVlZdTX1wOQnZ1Nc3MzW7ZsASAlJYW4uDiKi4u9kxcbS3Z2NgUFBTjnMDPy8/MpKSmhsbERgJycHBoaGqiqqtJ50nkasvM0MylAVqwDYHm1jzAfzEwKALC+wVhXb5yS5qXr2o1XK3wsSO8kyg8AS8p8HDbWkR7jtfF2lY/oMJg+xquzpt7Y2GicOMFLb2s1Xt/i44zMTsKDY7xPl/o4MsUxPtprY1mlj8QIx8GJXvrjOqOq2Zib6rVR3WIsq/SxMKsTAxzwdKmfOeMCJEd5dV7f4iMl2nFQgpf+qNaobTPmjPPa2NxsvFNlLMzy0u0Oni3zMzc1wJhIr84rFT4yYh1T4r30B9t8NHfAUSlenU1NxntbjTMzvXRLJ7ywyc/8CQESwr06L5b7yI13TIrz0u/W+OgIwOzkYBubNul6GkHX01Cfp10x59zAhWZfwZvcOsM5tzmY1wF8Be+2TSWwwDm3tEednwGnOuemm1kh8Ixz7oYe5acBzwFjnHO1u+rcrFmz3IoVK3Z7ECJyYNEL+fYvvZBPPikzW+mcmzVQ+e5GSm7Cu81S0mMKiB+4Hy/QbwUy+tTJANYH/10+QPn23QUkIiIicmDZ3ZySk4B8YEaPTyfeSpxDgbeAU7o2NrNwYB7eMmCAZcAJwfwup/QoFxEREQF2M1LinCvumxccMdnsnCsys58DT5vZlcBreKtz2oGHgpvfD1wD3Gtmd+IFLGcAx+6d7ouIiMhI8akeM++cW4K3VPhaYDmQDZzsnGsMllcApwMzgRV4zzE51zmniSIiIiLSy2BW3/TinAvrk74HuGcX27+Bd9tHREREZEB6IZ+IiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEhEEFJWb2BTP7wMyazKzMzG4xMwuWZZuZ6/Mp6lP/GDNbbmYtZrbWzM7fFwcjIiIiw1fYILfLA34KFAJHA3cDNcCvgSQgENzGBbdv66poZlnAUuAPwGXAF4BHzKzYOfefvXAMIiIiMgIMKihxzt3aI/mhmZ0FnIIXlIwBtjvn1g5Q/UpgI3C1c84Bq8zsdOCbwCWfuOciIiIyonzSOSU+vJES8IKSrbvYdj6wNBiQdHkJb8RFREREBBj87RsAzGwUcD5wFHBCMDsJmGxmzUA58DJws3OuOlieA/y+T1NlQPoA+7gcuBwgPT2dVatWAZCamkp0dDQlJSUAxMfHk5mZSUFBAQB+v59p06ZRXFxMU1MTALm5udTV1VFd7XVlwoQJhIeHU1paCkBCQgJpaWkUFhYCEB4eTl5eHmvXrqWlpQWAKVOmUFNTQ02NF4OlpaXh8/nYuHEjAImJiYwbN46iIm8aTUREBFOnTmX16tW0tXl3sfLy8qisrKS2thaAjIwMAoEA5eXl3heYlERSUhJr1qwBICoqismTJ1NUVER7ezsA06ZNo7y8nLq6OgCysrJob2+noqICgOTkZBISEli3bh0AMTEx5OTkUFhYSGdnJwD5+fmUlZVRX18PQHZ2Ns3NzWzZsgWAlJQU4uLiKC4uBiA2Npbs7GwKCgpwzmFm5OfnU1JSQmNjo3dyc3JoaGigqqpK50nnacjO08ykAFmx3t85y6t9hPlgZlIAgPUNxrp645Q0L13Xbrxa4WNBeidRfgBYUubjsLGO9BivjberfESHwfQxXp019cbGRuPECV56W6vx+hYfZ2R2Em5eG0+X+jgyxTE+2mtjWaWPxAjHwYle+uM6o6rZmJvqtVHdYiyr9LEwqxPDu9f9dKmfOeMCJEd5dV7f4iMl2nFQgpf+qNaobTPmjPPa2NxsvFNlLMzy0u0Oni3zMzc1wJhIr84rFT4yYh1T4r30B9t8NHfAUSlenU1NxntbjTMzvXRLJ7ywyc/8CQESwr06L5b7yI13TIrz0u/W+OgIwOzkYBubNul6GkHX01Cfp12x3gMYu9jQrAWIBBqAbzrnHgnmpwPj8OaVTAd+BFQAxzrnOsysE/iKc+4PPdq6FLjPObfLoGjWrFluxYoVg+qfiBw4Jt7w3P7uwgFtw6LT93cXZJgys5XOuVkDle/JSMkMIAGYBdxlZoc6565zzm0CNgW3ec/MioF/AUcAbwGteMFMT1HAjj3Yt4iIiIxwgw5KnHNdy3zfMbMm4Hdm9kPnXGOfTd8N/szCC0rKgYw+22QA6z9Bf0VERGSE+qQTXTsAA/z9lM0O/uy6ebQMb6VOTyfjzT0RERERAQYxUmJm8XhLfx8BNuPNG/kZ8Jhzrs7Mvos3h6QAmBYse9E51zUZ5JfAcjO7DfgL8D/AZODsvXwsIiIiMowN5vZNCxAO/BFvTkkpXpByR7B8B14gMhbveSQPAz/uquyce8/MvgAsAr4LfACc6pzbuJeOQUREREaA3QYlzrk2vNGNgcp/C/x2N208BTy1x70TERGRA4ZeyCciIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEgYVFBiZl8wsw/MrMnMyszsFjOzHuVfMbN1ZtZiZm+Z2fQ+9Y8xs+XB8rVmdv7ePhAREREZ3gY7UpIH/BQ4CrgNuBm4AsDMzgF+06O8BnjBzEYFy7OApcBbwBHAX4FHzOyIvXcYIiIiMtyFDWYj59ytPZIfmtlZwCnAr4EbgQeccw8AmNklQDlwHvAQcCWwEbjaOeeAVWZ2OvBN4JK9dSAiIiIyvH3SOSU+oMbMEoCZwPNdBc65auB94Ohg1nxgaTAg6fJSj3IRERGRwY2UdAnekjkf7zbNCcAkwICSPpuWAenBf+cAv99Fed99XA5cDpCens6qVasASE1NJTo6mpISb1fx8fFkZmZSUFAAgN/vZ9q0aRQXF9PU1ARAbm4udXV1VFdXAzBhwgTCw8MpLS0FICEhgbS0NAoLCwEIDw8nLy+PtWvX0tLSAsCUKVOoqamhpqYGgLS0NHw+Hxs3bgQgMTGRcePGUVRUBEBERARTp05l9erVtLW1AZCXl0dlZSW1tbUAZGRkEAgEKC8vByApKYmkpCTWrFkDQFRUFJMnT6aoqIj29nYApk2bRnl5OXV1dQBkZWXR3t5ORUUFAMnJySQkJLBu3ToAYmJiyMnJobCwkM7OTgDy8/MpKyujvr4egOzsbJqbm9myZQsAKSkpxMXFUVxcDEBsbCzZ2dkUFBTgnMPMyM/Pp6SkhMbGRu/k5uTQ0NBAVVWVzpPO05Cdp5lJAbJivb9zllf7CPPBzKQAAOsbjHX1xilpXrqu3Xi1wseC9E6i/ACwpMzHYWMd6TFeG29X+YgOg+ljvDpr6o2NjcaJE7z0tlbj9S0+zsjsJDw4m+7pUh9HpjjGR3ttLKv0kRjhODjRS39cZ1Q1G3NTvTaqW4xllT4WZnVigAOeLvUzZ1yA5CivzutbfKREOw5K8NIf1Rq1bcaccV4bm5uNd6qMhVleut3Bs2V+5qYGGBPp1XmlwkdGrGNKvJf+YJuP5g44KsWrs6nJeG+rcWaml27phBc2+Zk/IUBCuFfnxXIfufGOSXFe+t0aHx0BmJ0cbGPTJl1PI+h6GurztCvWewBjFxuatQCRQAPwTefcI2Z2HPAvYJJzrqTHtn8EJjjnTjKzTuArzrk/9Ci/FLjPObfLoGjWrFluxYoVg+qfiBw4Jt7w3P7uwgFtw6LT93cXZJgys5XOuVkDle/JSMkMIAGYBdxlZocCTwTLIvtsGwXsCP67dTflIiIiIoMPSpxzRcF/vmNmTcDvgLuDeRlAUY/NM4C3g/8uD6bpU75+j3srIiIiI9YnnejagTeXZDuwAW8lDgBmloQ3+fXlYNaynuVBJ/coFxEREdn9SImZxeMt/X0E2AxMB34GPOacqzOznwOLzew9oBD4MbAKeCHYxC+B5WZ2G/AX4H+AycDZe/lYREREZBgbzO2bFiAc+CPenJJSvCDljmD5r4ExwJ1ALPAicKZzLgDgnHvPzL4ALAK+C3wAnOqc27gXj0NERESGud0GJc65NrzRjYHKHXBr8DPQNk8BT32SDoqIiMiBQS/kExERkZCgoERERERCgoISERERCQkKSkRERCQkKCgRERGRkKCgREREREKCghIREREJCQpKREREJCQoKBEREZGQoKBEREREQoKCEhEREQkJCkpEREQkJCgoERERkZCgoERERERCgoISERERCQkKSkRERCQkKCgRERGRkKCgREREREKCghIREREJCQpKREREJCQoKBEREZGQoKBEREREQoKCEhEREQkJCkpEREQkJAwqKDGziWb2pJnVmVmNmT1lZhODZX4zC5iZ6/Fp6VM/z8xeNbMmM9toZlftg2MRERGRYSxskNvdDqwHTgASgDuAJWY2A0gEDJgHlAe3D3RVNLM44GVgGXA0cBzwSzPb5Jx76tMfgoiIiIwEgw1KvuOcK+tKmNm3gDeBqUBHMPtd51xDP3UvAqKBS5xzzcAHZnYicCWgoERERESAQd6+6RmQBHXdngkDxgBtAwQkAPOB14IBSZeXgKPMzPaksyIiIjJyfdKJrl8FNgMfA0lAhJk1m1mpmf25a75JUA5Q0qd+GRAVrCsiIiIy6Ns3AARHNr4PXA6c45xrN7N3gNlAK5AH3AL808wODY6exAI7+jTVFPwZ1c8+Lg+2T3p6OqtWrQIgNTWV6OhoSkq8+CY+Pp7MzEwKCgoA8Pv9TJs2jeLiYpqavOZzc3Opq6ujuroagAkTJhAeHk5paSkACQkJpKWlUVhYCEB4eDh5eXmsXbuWlhZvMGjKlCnU1NRQU1MDQFpaGj6fj40bNwKQmJjIuHHjKCoqAiAiIoKpU6eyevVq2traAMjLy6OyspLa2loAMjIyCAQClJd7U3CSkpJISkpizZo13pcSFcXkyZMpKiqivb0dgGnTplFeXk5dXR0AWVlZtLe3U1FRAUBycjIJCQmsW7cOgJiYGHJycigsLKSzsxOA/Px8ysrKqK+vByA7O5vm5ma2bNkCQEpKCnFxcRQXFwMQGxtLdnY2BQUFOOcwM/Lz8ykpKaGxsRGAnJwcGhoaqKqq0nnSeRqy8zQzKUBWrANgebWPMB/MTPKmsq1vMNbVG6ekeem6duPVCh8L0juJ8gPAkjIfh411pMd4bbxd5SM6DKaP8eqsqTc2NhonTvDS21qN17f4OCOzk/Dg+O7TpT6OTHGMj/baWFbpIzHCcXCil/64zqhqNuamem1UtxjLKn0szOrEAAc8XepnzrgAyVFende3+EiJdhyU4KU/qjVq24w547w2Njcb71QZC7O8dLuDZ8v8zE0NMCbSq/NKhY+MWMeUeC/9wTYfzR1wVIpXZ1OT8d5W48xML93SCS9s8jN/QoCEcK/Oi+U+cuMdk+K89Ls1PjoCMDs52MamTbqeRtD1NNTnaVfMObfbjQDMbDTwEN5k1y85554ZYLtMYAPwRefcX8ysEHjGOXdDj21OA54Dxjjnagfa56xZs9yKFSsG1T8ROXBMvOG5/d2FA9qGRafv7y7IMGVmK51zswYqH9RIiZklA6/hjXAc5pwrHmhb51yZmdUAWcGsciCjz2YZwPZdBSQiIiJyYBnsnJLfAluB43YVkACY2SRgLNA1TrMMOMHMwntsdgreMmERERERYBAjJWYWDXwWuBZI77NgZjtwZrCd5XgjIIuAj4Cu2zv3A9cA95rZnXjPMzkDOHZvHICIiIiMDIO5fZMc3O7O4KenXwD/wnu42gRgC/A8cJNzrg3AOVdhZqcDvwJW4I2gnOuc02QRERER6bbboCT4jJLdPU9klw9Bc869AcwYfLdERETkQKMX8omIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiOxlZ02fwIZFp3Pe4em98seMiuAX58/g6pMm91vvlGnjeOZbx7L6R5/h3ZtP5oxDxw9Fd0VCRtj+7oCIyEgS4fdx7alTe+WlxEXy9bk5nDcrnehwP3f/c91O9RbOSOOn5xzC7S+u5o211YwZFUFzW+dQdVskJCgoERHZi646cTIfVdQzPiGqO++wzESmpsZx+R9XcPvnpu9UJz4qjFs/ezA/eraQPy8vG8ruioQUBSUiIntJXmocFx6dxWfu+hcnHZTSnf+Pj7bwj4+2DFhvwSHjae8M8PjKjUPRTZGQNag5JWY20cyeNLM6M6sxs6fMbGKP8rPMrMDMWszsQzOb36d+npm9amZNZrbRzK7ay8chIrJfRYb5uOv8Gfzq1XVU1LXsUd2ZmYms3tLARcdM5M0b5vPvG+bzo8/mEx3u30e9FQlNg53oejuwHjgBOA+YCCwxM7+ZzQaeBB4BjgD+AzxjZhkAZhYHvAxUAUcDi4G7zOycvXgcIiL71c1nTGN7Uzu/W7Z+j+uOi49kyrg4DhofzxV/epefPP8xpx2Syq2fPXgf9FQkdA329s13nHPdNzrN7FvAm8BU4DrgZefcomDZ14EzgEuBW4GLgGjgEudcM/CBmZ0IXAk8tbcORERkf/nCERmcenAqp/3yDZzb8/p+n7GjtYNrH/+AgIP3N25nXHwU138mjxue/JDAJ2hTZDgaVFDSMyAJ6hqbDAPmA7f02LbdzF7HGxUhWP5aMCDp8hJwh5mZc5/kEhYRCR3fOiGXxJhwll13QndemN/HT885hNsW5jP15qW7rL+1sQ2gV/BRXNVIRJiPhOhwapva90m/RULNJ53o+lVgc/CTCJT0KS8DPhP8dw5eENK3PApIArZ+wj6IiISEL/7uHcL91ivvH1cfz50vrdnlBNcuK0tr+X8nTSYyzEdrRwCAKalx1O5oU0AiB5Q9CkrMzIDvA5cD5+AFFgA7+mza1KMsdoByemzTcx+XB9snPT2dVatWAZCamkp0dDQlJV78Ex8fT2ZmJgUFBQD4/X6mTZtGcXExTU1e87m5udTV1VFdXQ3AhAkTCA8Pp7S0FICEhATS0tIoLCwEIDw8nLy8PNauXUtLizcYNGXKFGpqaqipqQEgLS0Nn8/Hxo3eLPnExETGjRtHUVERABEREUydOpXVq1fT1ub99ZOXl0dlZSW1tbUAZGRkEAgEKC8vByApKYmkpCTWrFnjfSlRUUyePJmioiLa273/IU2bNo3y8nLq6uoAyMrKor29nYqKCgCSk5NJSEhg3Trv+QcxMTHk5ORQWFhIZ6f3rIP8/HzKysqor68HIDs7m+bmZrZs8f6nmZKSQlxcHMXFxd6Ji40lOzubgoICnHOYGfn5+ZSUlNDY2AhATk4ODQ0NVFVV6TzpPA3ZeZqZFCAr1htWWF7tI8wHM5O8X+brG4x19cYpaV66rt14tcLHgvROooLzRpeU+ThsrCM9xmvj7Sof0WEwfYxXZ029sbHROHGCl97Wary+xccZmZ2EB2OPp0t9HJniGB/tgAaWVfpIjHAcnOi1aQY7mls4NKaeQ7OgusVYVukjxg95CY6FWZ08XepnzrgAHVvL8JPDby6Ywd+Xr2VW2ijOOz6HPy1bR3KUY844rx+bm413qoyFWV663cGzZX7mpgYYE+nt95UKHxmxjinxXvqDbT6aO+CoFK/Opibjva3GmZleuqUTXtjkZ/6EAAnhXp0Xy33kxjsmxXnpd2t8dARgdnKwjU2bdD2NoOtpqM/Trthg756Y2WjgIbzJrl9yzj1jZilAJbDAObe0x7Y/A051zk03s0LgGefcDT3KTwOeA8Y452oH2uesWbPcihUrBtU/ETlwTLzhuf3dhd1ad9sCbnhqFU+s3NQrf9n1J/DEyk3c9fLaXvk5ybH8eGE+MzNHU9vUzp/eKeXX/1z3ieao7GsbFp2+v7sgw5SZrXTOzRqofFAjJWaWDLyGN8JxmHOuOFi0FWgFMvpUycBbrQNQPkD59l0FJCIiw1nuTS/0mz9n8T/7zS+ubuR/7n97X3ZJJOQNdknwb/ECkON6BCQ45wLAW8ApXXlmFg7Mw1sGDLAMOCGY3+WUHuUiIiIiuw9KzCwa+CzwNyDdzHJ7fMYCPwfOMbMrzewQ4B6gHe9WD8D9ePNK7jWzQ8zsSrwlw4v3wfGIiIjIMDWYkZJkvNs8dwJr+3z+1zm3BLgCuBZYDmQDJzvnGgGccxXA6cBMYAXwdeBc55wmi4iIiEi33c4pCT6jxHazzT14IyQDlb8BzNjTzomIiMiBY7BzSkRERET2KQUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiMhunHjiiTzzzDPd6cjISEpLS/n444+5554B30fbr9bWVmpqali/fj21tbV7u6vD2m7fEiwiInIgKysr47XXXuPBBx/szgsEApgZ4eHh3H777SxdupRHHnmE2NhYLrnkEpYvX05HRwcdHR20tbXR1tZGIBAgPDyciIgIRo0aRUxMDFdddRVf/vKX99/BhRgFJSIiIrtw3333MW/ePDIyMrrzOjs7CQsLIzMzk7feeouFCxdSUFDAUUcdxc9//nM6OjqIiIggIiKCyMhIrrvuOmJjY/nBD36w/w5kGFBQIiIiMoCmpibuu+++Xrdo2tvbcc4RGRkJQHJyMsuWLcPMePLJJ7niiit2aqehoQEz6/dWzy9+8Qu+8IUv7LuDGEYUlIiIiAzgrrvuorq6mgULFmBmvcrGjh270/bXX389W7Zs6U6/+eabXHjhhWRmZnbfwnn88ceZOnXqPu/7cKSJriIiIv3YtGkTixcvBsDMcM7hnOPjjz8mJiaGv/3tbxx++OHd+c45Fi1aBEBjYyPXX389l112GY8++ijHH388CxcuZPHixZx66qnceuut1NfX78/DC0kKSkRERPpx/fXXc8opp+yUX1pa2mt+SU/V1dX89Kc/5aCDDgJgxYoVHHHEEd3lCxYsYMWKFVRVVTFp0iQuv/xy3njjjX1zAMOQbt+IiIj047jjjuP888/niSee6JX/3nvvkZ2d3W+dP/3pT2zdupVvfvObFBcXc/XVVwPQ0tICwFe+8hUAbrjhBm666SYefPBBWltb991BDDMKSkRERPrx9a9/vd/8J554gi9+8Yv9lnUFIdXV1dTV1fG9732PadOmdS/7fe6557j77ru5/fbbGT16NDfeeOM+6ftwpds3IiIig/TOO++wcuVKzjrrrJ3K/vznP3PLLbcA3oqc3NxcNmzYwHHHHUdubi65ubk8+uijlJeXk5uby9ixY3n//feH+AhCm4ISERGRQXDOceWVV3LWWWeRk5OzU/nGjRtZu3Ztd3rVqlWsXr2aWbNmdee99dZb7Nixg61btxIVFUVMTMyQ9H240O0bERGRQbj22mt59913WbVqFeA9ar6yspKGhgZ8Ph8vv/wyc+bMAaCtrY1vf/vbXHbZZYwePbrf9rZt20ZiYuJQdX9YUFAiIiKyG48//jh33HEHP/7xj7tX1syePZvo6Gji4+MByMrK4r777qO6upqLL76Yuro6br311u422traqK+vJzIykldffZWoqKh+n3VyINuj2zdmNt3MVprZvB55fjMLmJnr8WnpUy/PzF41syYz22hmV+2d7ouIiOx78+bN45vf/CY33XRTd15SUhJr1qyhqamJpqYmNmzYQFNTE7m5ufh8Pv7xj390BywAFRUVpKSkEB8fz1e+8hXuvPPOnR7IdqAb1EiJmc0GrgVOB6L7FCcCBswDyoN5gR5144CXgWXA0cBxwC/NbJNz7qlP03kREZF9zTkHwN13391veXT0f38tHnzwwbz44osceeSRO203ceJEAoEAzjkFIwMY7O2bzwHNwBnAK33KxgR/vuuca+in7kV4gcwlzrlm4AMzOxG4ElBQIiIiI0p/AUlPCkgGNtig5DrnnDOzif2UjQHaBghIAOYDrwUDki4vAXeYmbmuEFREREQOaIMKSnYTOCQBEWbWDFQBbwI3Ouc2BMtz8IKQnsqAqGDdrT0LzOxy4HKA9PT07lnOqampREdHU1JSAkB8fDyZmZkUFBQA4Pf7mTZtGsXFxTQ1NQGQm5tLXV0d1dXVAEyYMIHw8HBKS0sBSEhIIC0tjcLCQgDCw8PJy8tj7dq13U/fmzJlCjU1NdTU1ACQlpaGz+dj48aNACQmJjJu3DiKiooAiIiIYOrUqaxevZq2tjYA8vLyqKyspLa2FoCMjAwCgQDl5d7drqSkpO57kwBRUVFMnjyZoqIi2tvbAZg2bRrl5eXU1dUB3oSq9vZ2KioqAG9NfEJCAuvWrQMgJiaGnJwcCgsL6ezsBCA/P5+ysrLu9y1kZ2fT3Nzc/fKolJQU4uLiKC4uBiA2Npbs7GwKCgq6hxvz8/MpKSmhsbHRO7k5OTQ0NFBVVaXzpPM0ZOdpZlKArFjvf0vLq32E+WBmknfXeH2Dsa7eOCXNS9e1G69W+FiQ3kmUHwCWlPk4bKwjPcZr4+0qH9FhMH2MV2dNvbGx0Thxgpfe1mq8vsXHGZmdhAf/yH261MeRKY7x0V4byyp9JEY4Dk700h/XGVXNxtxUr43qFmNZpY+FWZ0Y4ICnS/3MGRcgOcqr8/oWHynRjoMSvPRHtUZtmzFnnNfG5mbjnSpjYZaXbnfwbJmfuakBxkR6dV6p8JER65gS76U/2OajuQOOSvHqbGoy3ttqnJnppVs64YVNfuZPCJAQ7tV5sdxHbrxjUpyXfrfGR0cAZicH29i0SdfTCLqehvo87YrtyUBFcKSkBDjBOfdaMG8skA20AnnALUAMcKhzrsHMioFHnHO39GhnPt5toAzn3KaB9jdr1iy3YsWKQfdPRA4ME294bn934YC2YdHp+7sLMkyZ2Urn3KyByj/1kmDn3Fb+O9rxoZm9DWzAm3/yF7xgJbJPtajgzx2fdv8iIjKyKOjcv/Zn0LnXn+jqnCsDaoCsYFY50Pd1ihnAdudc7d7ev4iIiAxPez0oMbNJwFig6+bRMuAEMwvvsdkpeMuERURERIC9cPvGzC4JtrMcbwRkEfAR8Exwk/uBa4B7zexOvOeZnAEc+2n3LSIiIiPH3njMfB1wOzAB2AI8D9zknGsDcM5VmNnpwK+AFXgjKOc65zSDVURERLrtUVASXOZrffKeYjcPQXPOvQHM2MO+iYiIyAFkr88pEREREfkkFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhISFJSIiIhISFBQIiIiIiFBQYmIiIiEBAUlIiIiEhIUlIiIiEhIUFAiIiIiIUFBiYiIiIQEBSUiIiISEhSUiIiISEhQUCIiIiIhQUGJiIiIhIQ9CkrMbLqZrTSzeX3yzzKzAjNrMbMPzWx+n/I8M3vVzJrMbKOZXfXpuy4iIiIjyaCCEjObbWaPA28BM/uWAU8CjwBHAP8BnjGzjGB5HPAyUAUcDSwG7jKzc/bWQYiIiMjwN9iRks8BzcAZ/ZRdB7zsnFvknFsFfB3YAVwaLL8IiAYucc594Jz7NfB34MpP1XMREREZUQYblFznnLsQWN9P2Xzg+a6Ec64deB1vVKSr/DXnXHOPOi8BR5mZ7XmXRUREZCQKG8xGzjnXX76ZJQKJQEmfojLgM8F/5+AFIX3Lo4AkYGufNi8HLgdIT09n1apVAKSmphIdHU1Jiber+Ph4MjMzKSgoAMDv9zNt2jSKi4tpamoCIDc3l7q6OqqrqwGYMGEC4eHhlJaWApCQkEBaWhqFhYUAhIeHk5eXx9q1a2lpaQFgypQp1NTUUFNTA0BaWho+n4+NGzcCkJiYyLhx4ygqKgIgIiKCqVOnsnr1atra2gDIy8ujsrKS2tpaADIyMggEApSXlwOQlJREUlISa9asASAqKorJkydTVFREe3s7ANOmTaO8vJy6ujoAsrKyaG9vp6KiAoDk5GQSEhJYt24dADExMeTk5FBYWEhnZycA+fn5lJWVUV9fD0B2djbNzc1s2bIFgJSUFOLi4iguLgYgNjaW7OxsCgoKcM5hZuTn51NSUkJjY6N3cnNyaGhooKqqSudJ52nIztPMpABZsd7/lpZX+wjzwcykAADrG4x19cYpaV66rt14tcLHgvROovwAsKTMx2FjHekxXhtvV/mIDoPpY7w6a+qNjY3GiRO89LZW4/UtPs7I7CQ8+KfU06U+jkxxjI/22lhW6SMxwnFwopf+uM6oajbmpnptVLcYyyp9LMzqxAAHPF3qZ864AMlRXp3Xt/hIiXYclOClP6o1atuMOeO8NjY3G+9UGQuzvHS7g2fL/MxNDTAm0qvzSoWPjFjHlHgv/cE2H80dcFSKV2dTk/HeVuPMTC/d0gkvbPIzf0KAhHCvzovlPnLjHZPivPS7NT46AjA7OdjGpk379Ho6O6tT52kvnKfSRqNwu7Eg3Uvv6DBeLPdxSlqAUWFenRc2+Zg22vW6nrZt27ZP/7+3KzZAvNH/xmYT8QKQE5xzrwXnjZQB851z/+yx3Q+BC5xzuWZWDDzinLulR/l84BUgwzm3aaD9zZo1y61YsWLQ/RORA8PEG57b3104oG1YdPo+bV/nd//al+fXzFY652YNVP5plwS3Bn9G9smPwptX0rVNf+X02EZEREQOcJ82KNmKF3Rk9MnP4L/zT8oHKN/unKv9lPsXERGREeJTBSXOuQDeMuFTuvLMLByYh7cMGGAZcEIwv8spPcpFRERE9soTXX8OnGNmV5rZIcA9QDvwULD8fiAWuNfMDjGzK/GWFi/eC/sWERGREeJTByXOuSXAFcC1wHIgGzjZOdcYLK8ATsd76NoKvOeYnOuc0wxWERER6TaoJcFdnHMbgJ2eLeKcuwdvhGSgem8AM/awbyIiInIA0Qv5REREJCQoKBEREZGQoKBEREREQoKCEhEREQkJCkpEREQkJCgoERERkZCgoERERERCgoISERERCQkKSkRERAbpoPFxLPnWHI6aNKbf8vTEaFb/+DPc/rlDe+XPm5rM81fNYfWPPsMr18zlvMPTh6K7w84ePdFVRETkQDQ9PYHLj89hfl4K0RH+Abe77tSpRIb1Lp+YFMP9F87inteL+c5jH3BMThKLzz2U8tpm3lpfs6+7PqxopERERGQ3TjtkPC0dnVz60H8G3GZO7lgOnziGd0tre+VPmxBPY0sHd7y4hqItDfz+zQ18vLmeQ9IT9nW3hx0FJSIiIrvx0xeKuOaxD9i4ranf8uhwP7ednc+Pni2kqa2zV9lbxTV0BBxnTZ+Az+C4yWNJGx3NKx9XDkXXhxXdvhEREfmUbjlrGuuqGllasIUvHZnVq6y2qZ3vPv4Bv7/4CO76wgx8PuP6Jz+kuHrHfupt6FJQIiIi8imcceh4TjpoHAvueqPf8pzkWH51wWHc+3oxSz/awtGTkvjBmQdTWrODt9dvG+LehjYFJSIiIp/QQePj+Ok5h/CNR96lurG1322uPmky75dt52f/WA3Ah5vqyEoaxfWfyePs3/x7KLsb8hSUiIiIfEKXHpvNqIgwHrhoVndemN+brnnmoRP48u+Xc9D4eF4u7D1/ZFX5ds4+LG1I+zocKCgRERH5hH72j9Xc83px77zzprO1sZWfLS2ifHszW+payB0X22ubqanxVNa3DGVXhwUFJSIiIp9QdUMr1Q29b9s0t3XS0NLePZH1obc2cO+XDueKE3L5Z1EVsyYm8j+zM1j0QtH+6HJIU1AiIiKyD71UWMn/e+x9vjkvl6vm57K5roXFLxTxhzc37O+uhRwFJSIiIoO0qbaZiTc8t8ttvvTAOzvl/f39Cv7+fsW+6taIoYeniYiISEhQUCIiIiIhQUGJiIiIhAQFJSIiIhIS9kpQYmYnmJnr81nao/wsMyswsxYz+9DM5u+N/YqIiMjIsbdGSpKAcmByj88lAGY2G3gSeAQ4AvgP8IyZZeylfYuIiMgIsLeCkjFApXNuXY/P5mDZdcDLzrlFzrlVwNeBHcCle2nfIiIiMgLszaBk6wBl84HnuxLOuXbgdeDovbRvERERGQH25u2bk8xsh5l9bGaLzCzWzBKBRKCkz/ZlQPpe2reIiIiMAHvria73AH/GC3KOBn6IN6/k6mD5jj7bNwFR/TVkZpcDlwOkp6ezatUqAFJTU4mOjqakxItv4uPjyczMpKCgAAC/38+0adMoLi6mqakJgNzcXOrq6qiurgZgwoQJhIeHU1paCkBCQgJpaWkUFhYCEB4eTl5eHmvXrqWlxXtR0pQpU6ipqaGmpgaAtLQ0fD4fGzduBCAxMZFx48ZRVOS9wyAiIoKpU6eyevVq2traAMjLy6OyspLa2loAMjIyCAQClJeXA5CUlERSUhJr1qwBICoqismTJ1NUVER7ezsA06ZNo7y8nLq6OgCysrJob2+nosJ7QmBycjIJCQmsW7cOgJiYGHJycigsLKSzsxOA/Px8ysrKqK+vByA7O5vm5ma2bNkCQEpKCnFxcRQXey+Xio2NJTs7m4KCApxzmBn5+fmUlJTQ2NgIQE5ODg0NDVRVVek86TwN2XmamRQgK9YBsLzaR5gPZiYFAFjfYKyrN05J89J17carFT4WpHcS5QeAJWU+DhvrSI/x2ni7ykd0GEwf49VZU29sbDROnOClt7Uar2/xcUZmJ+HmtfF0qY8jUxzjo702llX6SIxwHJzopT+uM6qajbmpXhvVLcaySh8LszoxwAFPl/qZMy5AcpRX5/UtPlKiHQcleOmPao3aNmPOOK+Nzc3GO1XGwiwv3e7g2TI/c1MDjIn06rxS4SMj1jEl3kt/sM1HcwccleLV2dRkvLfVODPTS7d0wgub/MyfECAh3KvzYrmP3HjHpDgv/W6Nj44AzE4OtrFp0z69ns7O6tR52gvnqbTRKNxuLEj30js6jBfLfZySFmBUmFfnhU0+po12va6nbdu27dP/7+2KOed2u9GeMrMvA38EJgIbgAXOuZ6rcX4GnOqcm76rdmbNmuVWrFix1/snIsPb7h7zLfvWhkWn79P2dX73r315fs1spXNu1kDl++o5Je8Gf6YBrUDflTYZwPp9tG8ZQNdf4iIiIqFoXwUls4EAsA54Czilq8DMwoF5wMv7aN8CHHXUUbz22mu98o4//niee05/gYiISGjaK3NKzOw2vOePrAeOBBYDDzjnqszs58DTZnYl8BrePJN24KG9se/hoKWlpfue+mBt2bKFgw46aLfbpaWlsWnTpt1ut3z5ct566y2qq6t54oknuvNjYmI47bTT9qhvIiIi+8LemugaAO4FEvACkx8DvwBwzi0xsyuAG4Gf4Y2cnOyca9xL+w55ixYt4tZbb92jOjfeeCPNzc0A/P73v+fRRx/lxRdf7LXNtdde2z1BcHd+9rOfMWnSJB555JHuvNLSUjo6OronO4qIiOxPeyUocc7dDNy8i/J78FboHJB+8IMf8IMf/GCn/KeeeooLLriAFStWkJ+fP2D9trY2/H4/UVH/XbBUV1fHQw89xLPPPttr2wULFvDGG2/Q3NzMggUL8Pv9PPnkk7zyyiuMHz+eF154gfDwcBoaGjjssMO4/vrr99pxioiIfBp6Id9+Ul5ezle/+lUWL168y4AEvADk3//+d/cySoB7772XKVOmcPzxx/fa9oUXXqCuro4xY8bwwgsvUFtbyxVXXMEDDzzA4YcfzjXXXENzczOf+9znOPjgg/na1762T45PRERkTyko2Q927NjB2WefTUdHB5MmTdrtqpjKykra2tp48MEHu/OWLl3KypUrMTPMjMrKyu6yt956i61bt/Lb3/6W5uZmbrvtNs455xzuvvtu/vnPf5KXl0dLSwt//etfMbN9dZgiIiJ7REHJEGtra+Pcc89lzJgxnHrqqbz55pv88Ic/5MQTT+x+GFZf69ev5+KLL+bOO+/snmeydOlSmpub+e1vf8uhhx5KSkpK9/Z///vfAWhqauKzn/0sCxcuxDnHX//6VzZv9l5JVFJSwh/+8Ifuh2vJ8KBl3SIykikoGULbt2/n1FNPpaWlhb/97W9EREQAcPXVV5Oens4hhxzC4sWL6ejo6FVv1apVfPazn2XevHksXrwY8J6g6Pf7Wbx4Md///ve7Rzy2b9/OY489xqGHHso111zD/PnzeeCBB5gxYwb/93//x5/+9Cc2bNjAj370I37605+SlJTEjTfeOLRfhAyKlnWLyIFGQckQKSgo4JhjjsHMWLJkCdHR0d1liYmJPPTQQzzxxBPceeedHHHEEaxcuRKAjz76iIqKCo455hhuueUWfvGLX3SX3XDDDaSmpnLOOed0t3XXXXdx8sknd7c/b948fvOb33DNNddQWFjIqaeeiplx4YUXUlJSwqOPPsr8+fOH8Js4sBQVFXXfYtvVJz1996+C6rusu+vz/PPP77auiMhwsLeWBMsu/OIXv+CGG27gO9/5Dj/84Q/x+/39bnfaaafx4Ycfcskll7BkyRIOP/xwHnjgAY499lhSUlJISUnhN7/5DWeddRbf+MY3ePjhh7vnlXRZuXIld9xxBxdeeCEAxx13HEuWLGHSpElceumlvfbnnONzn/scjz766L47+APc1KlT99rSbi3rFpGRTkHJEBg7dixPPvnkoB5SlpKSwnPPPUcgEKCsrIx77rmHP/7xj93l559/Pr/5zW+4+eab+cEPfkBGRu8n+N9///2kpqb2yjMzkpKS2Lp1a6/83/3ud7z8sh6suy+ZWfdS7sEu7daybhE5UOn2zRD44he/uMdPTXXOcfHFFzNr1izOPfdcAAoLCznuuOOoqKjguuuu46c//SkLFizgP//5T3e9vgGJhI7BLu3Wsm4ROVApKAlRt9xyC++//z4PPfQQS5cuZcGCBcycOZPDDjuMDz/8kMWLF1NQUMDo0aM5+uijycjI4J57Bn4+XU1NDWFhYb0++mU2tPZkabeWdYvIgUhBSYg677zzeOqpp8jOzqaoqIj8/HzWrVvHr371K0aNGgVAbm4uf/nLX9iwYQNXXHEFxx57bHf9sLCwXr+skpKS6Ojo6PW59957h/y4DmR7srRby7pF5EBkzrn93YcBzZo1y61YsWJ/d0Nkr0hLS+Puu+/miSeeIDc3t/vVA+3t7UyZMoXbb7+dc889l+3btzN9+nRGjx7NL37xC9544w2SkpK49957aW5u5le/+hWnnHIKDz/8MP/7v/9LZWUl11xzDT/5yU/27wEOoYk3aFn0/rRh0en7tH2d3/1rX55fM1vpnJs1ULkmuooMgZ5Luw8++GBmz57NmWeeyeGHH77T0u6uZd0FBQWAt6z7G9/4Btdddx0XXHABYWHeZXvhhRfyxS9+kWeeeYa4uLj9dmwiInuLbt+IDIGeS7snT57cvbT7xz/+MQ8//DCPPfZY9+22lStXct1113XX7VrWfdFFFxEVFdVrXlBERAR//etfOemkk/bXoYmI7DUKSkT2sa6l3VdffXV33vnnn8+kSZO4+eabueKKK3ot7b7//vuZMmVKrza6lnVrXpCIjGQKSkT2oc7Ozj1e2q1l3SJyoDpg55RoItX+ta8nyoWKrqXdK1euZOnSpfzyl7/kn//8J1/96lf5xz/+wahRo/jqV7/KzTffzNFHH8348eO56aab+PrXv75TW13LunvqeiqviMhIoJESkX3o0yzt1rJuETnQHLBLgjVSsn8dKCMlsm/o+t2/tCR4ZNufS4I1UiIiIiIhQUHJMHDQ+DiWfGsOR00a0yv/m/NyeOt78yn60Wd45LIjyRgTvZ96KCIi8ukpKAlh09MTuPuCmTz1jWM5JD2hV9llc7L5+rwcFr1QxAX3v43fZ/zh4tmE+fQeFBERGZ4UlISw0w4ZT0tHJ5c+9J+dyr4xL4d7Xivm7+9X8G7Zdq76y3tkjIlm7tTk/dBTERGRT++AXRI8HPz0hSIA0hN735ZJjAlnbGwkH1XUd+dVN7ayvnoHMzJG88rHVUPaz1A0nCbKHTQ+jp+dO53bni/k7fXbuvNPOiiFa0/NY2JSDOu37uCHzxbyVnHNfuzp4Gkis4h8EhopGYYaWjpo7wyQNrp3sBIbGUbSqMj91CvZU7u6PTc9PYHffulwnn6vnLN+/SYfbqrjdxfOYnxC1H7qrYjIvjckQYl5bjSzTWbWbGYvmlnWUOx7JOoIOF78qJKvzZ3E5JRYIvw+vjU/l/EJUQRCeIm39Lar23Nfm5vDm+u28tvXi1ld2cBNf1tFU1snn5+V0U9LIiIjw1DdvrkauA64FNgI/Br4u5kd5kL5QSkh7Pt/L+DOL8zgpe/MJRBwvPRxJavK66hpbN3fXZNBGuj2HMAxOUnc+fLa7nRHwPFOSQ0zM0cPVfdERIbcPg9KzMwH3AD81Dn3VDDvcuB94Hjg9X3dh5GoZkcbF/5+OfHRYYT7fGxrauOd753Ya56JDE/x0WGMjolg47amXvkV21uYO0UTmUVk5BqK2zf5QArwfFeGc+4DoBo4egj2P6LVN3dQs6ONUw9OJSLMx7/WVu/vLsmnNCrC+1uhua2zV35zeyeRYZoGJiIj11DcvskJ/izpk18GpA/B/keko3OSwHmrbmZmJvK9BXnc9vzHtLQH9nfX5FNq6/DOYUSfACQyzEdze2d/VURERoShCEpigz939MlvAnZaShC8tXN5MNloZqsHuZ+xwNZP1MMQFzllSgTXrz6k8Znb1pQ+91wDwGevuirp+9//fnpcXJy/pKSk5YbvXLnlvvvu27a7tkKFLd7jKgfM+d3o89F6XdPMsR/9taz05z/vPua0Y5/J3lQQ5StdfEbx/uzvYOj8jmw6vyPbPj6/u1zkss9fyGdmnwf+CkQ751p65C8H3nLOfXsv7WfFrl7yI8PbCD+/E/FGEk8AXgvm/RPvIv9cMB2ON7r4Y+Duoe3evjfCz+8BT+d3ZNub53coblCXB3/2XcuYAawfgv2LDEc/B84BrgQOAe4B2oGH9menRET2paEISt4FmoFTujLM7BAgFXh5CPYvMhwtAa4ArgWWA9nAyUDj/uyUiMi+tM/nlDjnms3st8APzGwD3qqbXwFPO+c+2ou7um8vtiWhZySf3w1Af29SvCf4ORCM5PMrOr8j3V47v/t8TgmAmUUAdwBfwhudeRK42jmnh2qIiIgIMERBiYiIiMju6ElMIiIiEhKGdVCiF/2NPGY20cyeNLM6M6sxs6fMbGKwzG9mATNzPT4tu2lSQoSZndDn3DkzW9qj/CwzKzCzFjP70Mzm78/+yp4xs3n9nN+uz826focnM5tuZivNbF6f/F1er2aWZ2avmlmTmW00s6sGs79hHZTw3xf9XYX3Hp0EvBf99TdpUIaH2/GWip8AnIf3DI8lZuYHEvEmhM4DJgc/0/ZLL+WTSMJ7RMDkHp9LAMxsNt5cs0eAI4D/AM+YmV6LPHy8Q+9zOxk4G+gEHkPX77BiZrPN7HHgLWBm3zJ2cb2aWRze6toqvNfJLAbuMrNzdrvf4TqnJPiiv83Az51zi4N50/Fe9DfPOacX/Q1DZpbpnCvrkT4GeBM4GOgAVgPxzrmG/dRF+YSCT2v+mnPu8H7KngBGOecWBNPhwCbgN865W4e2p7K3mNmTQKNz7iIzm4Ku32HDzP4PGAc8CLwCnOCcey1Ytsvr1cy+BdwKpDvnmoPb/A0Y7Zw7YVf7Hc4jJXrR3wjUMyAJ6hreDQPGAG36H9qwNYaBH0U9n97XcjveG8R1LQ9TZjYDOAv4YTBL1+/wcp1z7kL6f8jp7q7X+cBrXQFJ0EvAUbu7kzGcgxK96O/A8FW8EbGP8Yb/I4Lzh0rN7M9d801kWEgCTjKzHWb2sZktMrNYM0vEG9rXtTyyXAc845zreleTrt9hxA1wG2WQ12vOAOVReP8dDGgoXsi3r+zRi/5keAlG09/HeznjOc65djN7B5gNtAJ5wC3AP83sUP31NSzcA/wZ74+ho/H+gp6MNzcMdC2PGGY2Hm9O2Kk9snX9jgyD+d0bO0A57OaaHs5BSWvwZyT/HeIH74D7fhkyjJjZaLx3vJwAnO2cewbAObeV/w7/f2hmb+M9DfUM4C9D31PZEz3+YgZYaWZ1wB+B7wTzIvtU0bU8fH0FWO+c+2dXhq7fEaPn796eel6vrQOUw26u6eF8+0Yv+huBzCwZb2LrBOCwroCkP8H5JzXs5lXYErLeDf5Mw/ufmK7lkeN8vNUZA9L1O2xtZffXa/kA5dudc7W7anw4ByV60d/I9Fu8/+iP6/OX9U7MbBIwFlg3FB2TvW42EMA7f2/R+1oOx1s6qmt5mDGzqXhLfZ/ezXa6foch51yA3V+vy4ATgvldTmEQ1/OwvX0zhC/6kyFiZtHAZ/HejJveZ5L2duBMvP9ml+NF3YuAj4ABR1MkdJjZbXjPM1gPHIn37IIHnHNVZvZz4GkzuxJ4DW+eSTvebTwZXubjzR9Y2TPTzC5B1+9Isbvr9X7gGuBeM7sTL2A5Azh2dw0P26Ak6HtABN4DXLpf9Lc/OySfSjLef5N3Bj89/QL4F97D1SYAW/CWpN3knGsbyk7KJxYA7sV7yOF64Md45xXn3BIzuwK4EfgZ3l9iJzvnGvdTX+WTOwJYFfyLuqc6dP2OCLu7Xp1zFWZ2Ot5AwQq80bBznXMrdtf2sH14moiIiIwsw3lOiYiIiIwgCkpEREQkJCgoERERkZCgoERERERCgoISERERCQkKSkRERCQkKCgRERGRkKCgREREREKCghIREREJCQpKREREJCT8f2h21jbGT6OtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "female_jump = femaleScore['女跳远分数']\n",
    "plt.figure(figsize=(9,6))\n",
    "count,bins,fig = plt.hist(female_jump,bins=4,rwidth=0.8)\n",
    "labels = ['不及格','及格','中等','优秀']\n",
    "_ = plt.xticks(np.arange(0,101,step=25),fontsize=14)\n",
    "_ = plt.yticks(np.arange(0,500,step=50),fontsize=14)\n",
    "for i,bar in enumerate(fig):    \n",
    "    h = bar.get_height()\n",
    "    w = bar.get_width()\n",
    "    plt.text(x = bar.get_x() + w/2 ,\n",
    "             y = h + 10,s =labels[i] ,ha = 'center')\n",
    "    plt.text(x = bar.get_x() + w/2 ,y=h/2-10,s=int(count[i]),ha='center',color='white')\n",
    "plt.grid(axis='y',linestyle='--',alpha=0.7)\n",
    "plt.title('   女跳远分数统计结果   ',backgroundcolor='#CD853F',color='white',pad=20,fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8978ee9b",
   "metadata": {},
   "source": [
    "### 男女生体重指数比例统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d68f25f2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maleScore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "54bb638e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "477"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = maleScore['BMI'].size\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4056ce36",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/1418237696.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  maleScore['BMI等级'][i] = \"超胖\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/1418237696.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  maleScore['BMI等级'][i] = \"正常\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/1418237696.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  maleScore['BMI等级'][i] = \"低体重\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/1418237696.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  maleScore['BMI等级'][i] = \"肥胖\"\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "477",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\range.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    384\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 477 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3564/1418237696.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmaleScore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BMI等级'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mmaleScore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BMI'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[1;36m16.4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m         \u001b[0mmaleScore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BMI等级'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"低体重\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmaleScore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BMI'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m16.4\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmaleScore\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BMI'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m<=\u001b[0m\u001b[1;36m23.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    941\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         \u001b[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexes\\range.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    385\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    388\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 477"
     ]
    }
   ],
   "source": [
    "maleScore['BMI等级']=''\n",
    "for i in range(n):\n",
    "    if maleScore['BMI'][i]<=16.4:    \n",
    "        maleScore['BMI等级'][i] = \"低体重\"\n",
    "    elif (maleScore['BMI'][i]>16.4) & (maleScore['BMI'][i]<=23.2):\n",
    "        maleScore['BMI等级'][i] = \"正常\"\n",
    "    elif (maleScore['BMI'][i]>23.2) & (maleScore['BMI'][i]<26.4):\n",
    "        maleScore['BMI等级'][i] = \"超胖\"\n",
    "    else:\n",
    "        maleScore['BMI等级'][i] = \"肥胖\"\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "358e403b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BMI等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
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       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>超胖</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>低体重</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>超胖</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI BMI等级  \n",
       "0      0  2785      62  170  72.599998  25.120001    超胖  \n",
       "1     60  3133      68  174  52.700001  17.410000    正常  \n",
       "2      0  3901      80  169  46.500000  16.280001   低体重  \n",
       "3      0  4946     100  183  79.699997  23.799999    超胖  \n",
       "4     68  3538      74  171  54.700001  18.709999    正常  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maleScore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1130948e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     354\n",
       "超胖      61\n",
       "肥胖      38\n",
       "低体重     24\n",
       "Name: BMI等级, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weightLevel_male = maleScore['BMI等级'].value_counts()\n",
    "weightLevel_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ca5475c0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/2204432466.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  femaleScore['BMI等级'][i] = \"正常\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/2204432466.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  femaleScore['BMI等级'][i] = \"超胖\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/2204432466.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  femaleScore['BMI等级'][i] = \"低体重\"\n",
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_3564/2204432466.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  femaleScore['BMI等级'][i] = \"肥胖\"\n"
     ]
    }
   ],
   "source": [
    "n = femaleScore['BMI'].size\n",
    "femaleScore['BMI等级']=''\n",
    "for i in range(n):\n",
    "    if femaleScore['BMI'][i]<=16.4:    \n",
    "        femaleScore['BMI等级'][i] = \"低体重\"\n",
    "    elif (femaleScore['BMI'][i]>16.4) & (femaleScore['BMI'][i]<=22.7):\n",
    "        femaleScore['BMI等级'][i] = \"正常\"\n",
    "    elif (femaleScore['BMI'][i]>22.7) & (femaleScore['BMI'][i]<25.3):\n",
    "        femaleScore['BMI等级'][i] = \"超胖\"\n",
    "    else:\n",
    "        femaleScore['BMI等级'][i] = \"肥胖\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e70105fd",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>BMI等级</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>超胖</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>超胖</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>正常</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>超胖</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI BMI等级  \n",
       "0     85  3775     100  163.0  51.299999  19.309999    正常  \n",
       "1     66  3683     100  163.0  66.599998  25.070000    超胖  \n",
       "2     76  3331     100  157.0  60.000000  24.340000    超胖  \n",
       "3     85  3701     100  160.0  50.700001  19.799999    正常  \n",
       "4     70  3592     100  167.0  63.900002  22.910000    超胖  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "femaleScore.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "668d029f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     441\n",
       "超胖      93\n",
       "肥胖      44\n",
       "低体重     15\n",
       "Name: BMI等级, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weightLevel_female = femaleScore['BMI等级'].value_counts()\n",
    "weightLevel_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8c110def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '   男女生体重指数比例统计   ')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "labels = ['正常','超重','肥胖','低体重'] \n",
    "colors = ['#4169E1', '#ffa500', '#c5b783', '#FF00FF']\n",
    "wedge1,text1,autotext1 = plt.pie(weightLevel_male,radius=1,autopct='%0.2f%%',pctdistance=0.8,labels=labels,\n",
    "            wedgeprops={'width':0.4,'linewidth':5,'edgecolor':'white'},colors=colors)\n",
    "wedge2,text2,autotext2 = plt.pie(weightLevel_female,radius=0.6,autopct='%0.2f%%',pctdistance=0.7,\n",
    "           wedgeprops={'width':0.4,'linewidth':3,'edgecolor':'white'},colors=colors)\n",
    "#plt.legend(['正常','超胖','肥胖','低体重'])\n",
    "plt.legend(handles=wedge1,\n",
    "           loc='lower center',\n",
    "           ncol=4,\n",
    "           labels=labels,                     \n",
    "           fontsize=14,\n",
    "           bbox_to_anchor=(0.5,-0.06)\n",
    "          )\n",
    "plt.text(x=0,y=0.8,s='男 生',fontsize=16,color='white',ha=\"center\",fontweight='bold')\n",
    "plt.text(x=0,y=0.4,s='女 生',fontsize=16,color='white',ha=\"center\",fontweight='bold')\n",
    "plt.title('   男女生体重指数比例统计   ',backgroundcolor='CornflowerBlue',color='white',fontsize=20)\n",
    "#display(wedge1,text1,autotext1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a60cad29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
